Systems, devices, and methods for tracking abdominal orientation and activity for prevention of poor respiratory disease outcomes

ABSTRACT

The disclosed apparatus, systems and methods relate to tracking abdominal orientation and activity for purposes of preventing or treating conditions of pregnancy, respiratory diseases or other types of medical conditions. In certain specific embodiments, the system, device, or method relates to identifying abdominal or sleep position orientation risk values, calculating and updating a cumulative risk value, comparing the cumulative risk value to a threshold, and outputting a warning when the cumulative risk value crosses the threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation-in-part of U.S. application Ser. No. 15/855,578,issuing Dec. 1, 2020 as U.S. Pat. No. 10,849,549, filed Dec. 27, 2017and entitled Systems, Devices, and Methods for Tracking AbdominalOrientation and Activity,” which was a continuation-in-part of U.S. Pat.No. 9,918,673 issued Mar. 20, 2018 entitled “Systems, Devices, andMethods for Tracking Abdominal Orientation and Activity,” which claimedpriority to U.S. Pat. No. 9,585,614 entitled “Systems, Devices, andMethods for Tracking Abdominal Orientation and Activity,” issued Mar. 7,2017, which claimed priority to U.S. Provisional Application 61/971,438,filed Mar. 27, 2014 and entitled “A Method and Device to Assess andAlter Abdominal Orientation;” U.S. Provisional Application 61/986,665,filed Apr. 30, 2014 and entitled “Systems, Devices, and Methods forTracking Abdominal Orientation and Activity;” U.S. ProvisionalApplication 62/022,060, filed Jul. 8, 2014 and entitled “Systems,Devices, and Methods for Tracking Abdominal Orientation and Activity;”U.S. Provisional Application 62/059,557, filed Oct. 3, 2014 and entitled“Systems, Devices, and methods for Reducing Preterm Birth in PregnantWomen;” and U.S. Provisional Application 62/111,427, filed Feb. 3, 2015and entitled “Systems, Devices, and Methods for Tracking AbdominalOrientation and Activity,” this application also claims priority to U.S.Provisional Application 63/056,257, filed Jul. 24, 2020 and entitled“Systems, Devices, and Methods for Tracking Abdominal Orientation andActivity for Prevention Of Poor Respiratory Disease Outcomes,” all ofwhich are hereby incorporated by reference in their entirety under 35U.S.C. § 119(e).

TECHNICAL FIELD

The disclosed devices, systems and methods relate to sensingtechnologies and algorithms to assess and treat clinical conditionsrelated to the abdomen and monitor pregnancy health and outcomes.

BACKGROUND

The disclosure relates to apparatuses, systems and methods formonitoring the abdomen during pregnancy, and assessing fetal risk,particularly while sleeping.

Medical evidence suggests that large amounts of mass or pressure in theabdominal region can lead to serious health consequences. Two veryclosely related examples of this are intra-abdominal hypertension(“IAH”) and abdominal compartment syndrome (“ACS”). In these conditions,fluid within the abdominal space accumulates in such large volumes thatthe abdominal wall stretches to its elastic limit. Once it can no longerexpand, additional fluid leaking into the tissue results in a rapidrises in the pressure within the closed space. Initially, this increasein pressure causes mild to moderate organ dysfunction (as seen in IAH).If the pressure continues to rise to higher levels, organs may begin tofail completely (as seen in ACS), which can lead to death.

A similar pathogenesis is observed in pregnant women, who also can havenegative clinical responses to their large abdominal masses. The abdomenas a whole may apply different amounts of pressure on intra-abdominaltissues and organs depending upon its orientation. Compression ofabdominal veins by the growing uterus explains many symptoms ofpreeclampsia. Inferior vena cava (“IVC”) compression has been shown todecreased flow in the uterine, portal, hepatic, splenic, and renalveins. This reduced flow directly or indirectly contributes to lowerextremity edema, fetal-placental ischemia, a glomerulopathy withproteinuria, and hypertension. Placental-fetal ischemia can lead toexpression of soluble fms-like tyrosine kinasel (“sFLT”) and endoglinwhich have been shown to cause additional diffuse endovascular damage.Increased renal venous pressure could lead to an increased release ofrenin with conversion of angiotensinogen to angiotensin and secretion ofaldosterone, leading to sodium and water retention, and increasedsystolic and diastolic blood pressure. In addition, IVC flow restrictionwould be expected to decrease venous return from the lower extremitiesand lead to the peripheral edema commonly present in preeclamptic women.

Preeclampsia is significantly more common in women whose abdomen has notbeen previously stretched, in severely obese women, in women who have alarge time gap between pregnancies, in multi-fetal pregnancies, and istwice as frequent in women with preexisting chronic hypertension.Further, preeclampsia almost never occurs prior to 20 weeks (when fetusstarts to gain significant mass), and its risk increases incrementallyevery week until delivery at which point it precipitously drops toalmost zero. Women with preeclampsia have a 40% increased incidence ofdelivering a baby with high birth weight for gestational age.Additionally, preeclampsia rates are significantly higher in women withhypolumbarlordosis, a purely mechanical abnormality of the spine thatpositions the uterus in more direct contact with the IVC.

Traditionally, obstetricians have advised pregnant women withpreeclampsia or other hypertensive disorders to avoid lying in thesupine position and to go on bed rest for periods of time; however,these recommendations are often incomplete as they only frame the issuein terms of “good positions” (e.g. bed rest, laying on left side) and“bad positions” (e.g. laying supine). As a result, there is a need inthe art for a method of developing more tailored recommendations forpregnant women with preeclampsia or other hypertensive disorders.

Various prior art home-use wearable pregnancy devices monitor metricssuch as fetal heart rate and uterine contractions. This data may improveresultant care in a small number of cases, they typically result inurgent reactionary medicine in the last month of gestation, andfrequently generate false positive alerts. In contrast, the presentlydisclosed devices, systems and methods were developed on the premisethat there are a large number of user physiologies and behaviors thathave profound impacts on pregnancy outcomes that can be monitored andimproved throughout pregnancy.

Many complications of pregnancy which cannot simply be fixed once theymanifest. Despite decades of research into the etiology of preeclampsia,its exact pathogenesis remains uncertain; however, it is becomingincreasingly apparent that preeclampsia is directly related tounderlying placental pathology and dysfunction that develop over manymonths. Because preeclampsia is not a disease that can be fixed at theend of pregnancy, prevention is important. The various implementationsof the system discussed herein relate to the acquisition of risk factordata relating to the user's physical position, such as when sleeping,snoring and the like.

As most cases of preeclampsia occur at the end of pregnancy, the bestway to prevent the majority of cases is to delay onset of dangerouslyhigh blood pressure until the pregnancy is full-term. The presentlydisclosed system tracks the adjustable factors in pregnancy that canprevent diseases and negative conditions. By way of example, theseadjustable factors include the following. Sleep disordered breathingleads to hypertension in both pregnant and non-pregnant individuals.Similarly, snoring and sleep apnea are highly correlated to hypertensivediseases of pregnancy such as preeclampsia, as well as gestationaldiabetes and premature birth. Uterine artery blood flow and fetaloxygenation are both shown to significantly decrease in the supineposition. Numerous studies have now shown that women who spendsignificant time in reclined and supine positions have much higher ratesof stillbirth and intrauterine growth restriction. Depression and stressdouble the risk of preterm birth even after covariants such as loweconomic status and drug use are excluded. This is not surprising asdepression is known to cause hypertension and increase placental andfetal cortisol levels.

The placenta serves as the lungs, kidneys, liver, endocrine system, andimmune defense of the fetus. Despite its enormous importance, theplacenta is arguably the least understood of all human organs. This ispartly because the development of the human placenta cannot beinvasively studied because it only exists during pregnancy at which timeit is performing life-maintaining duties for the fetus. Notsurprisingly, as we learn more about this mysterious organ, we alsolearn of its great importance in protecting the fetus from potentialhazards during the pregnancy.

There is a need in the art for improved fetal monitoring devices,systems and methods.

BRIEF SUMMARY

Discussed herein are various pregnancy health improvement systems,methods and devices. In some embodiments, a wearable device forcapturing certain physiological parameters and delivering feedback tothe user is provided. The device may include one or more sensors whichdetermine certain physiological parameters and a microcontroller thatreceives and stores orientation data from the sensors and uses analgorithm (and/or is configured) to estimate the level of clinical riskover various time scales based on those parameters. The wearable devicemay further include a communication device which conveys periodicupdates and alerts to the user on their current risk level, eitherinstantaneously or cumulatively.

Physiological parameters can include abdominal/body orientation,snoring, blood oxygen, blood pressure, location of center of gravity,activities like running, walking, driving, sleeping, body heat, altitudetracking, pressure readings, temperature readings, and/or user pedometerreadings, breathing during sleep, fetal activity monitoring (fetalkicks), fetal heart rate monitoring, uterine contraction monitoring, andfetal ECG monitoring, respiration characteristics, tension, temperature(measured at any variety of locations on or in the body), andhemodynamic flow restriction.

In certain embodiments, the present system may be used as a preventativemeasure in high risk pregnancies to slow the progression of placentalvascular pathology. In other embodiments, the device may be used toprevent or treat pregnancy complications or other abdominal-relatedconditions. The device could be specialized for each user depending on avariety of factors, including, but not limited to, user's height,weight, age, length of gestation, blood pressure, diagnostic testresults, time since diagnosis, prior number of device alerts, or doctorand user preference regarding the restrictiveness of their dailyactivities. The device may be strapped to the body with elastic, Velcro,or other straps, or the device may adhere to the user with an adhesive.Alternatively, the device may be a mobile phone with a specializedapplication installed or may be configured to connect wirelessly to acell phone with an application installed.

In certain implementations, the system is able to collect data. Infurther embodiments, the device may use geometric approximations and/orempirical reference data to determine the force, impulse, or pressurebeing applied to certain intra-abdominal tissues or organs by theabdomen. These tissues and organs may include the spine, kidneys, liver,bladder, all abdominal blood vessels including the inferior vena cava,and all abdominal nerves including the renal sympathetic nerves. Theforces determined might include acceleration forces, which measurewalking, running, and other movements that can impact clinical risk.This would allow healthcare professionals to recommend reducing certainphysical activities in order to reduce risks to the user. Optionally, amicrocontroller could store and transmit the data to the user or to careproviders electronically. The device could also be configured to sendrecommendations to the user or doctor to test blood pressure, urineprotein, or other markers of preeclampsia.

The device could be calibrated either automatically, by recognizingcertain characteristic position or movement data, or manually, ensuringoptimal data collection.

Various implementations comprise a variety of different sensors fordetecting physiological parameters, including those relating toorientation, blood flow, sleep patterns, and psychological condition.The device may include one or more sensors which determine the spatialorientation of the user's abdomen relative to the direction of Earth'sgravity and a microcontroller that receives and stores orientation datafrom the sensors and uses the data to estimate the level of clinicalrisk over various time scales. Optionally, the sensor data comprises arecline angle θ and a side tilt angle φ. The orientation risk values maybe a function of the recline angle and the sideways tilt angle.

In further embodiments, the device may include a variety of sensors andaccelerometers, which may be capable of monitoring the orientation ofabdominal soft tissue, various areas of the torso, or fetal heart rate.

In other embodiments, sensors could be used to monitor blood pressure.In one embodiment, the blood pressure monitor uses pulse wave transittime to estimate absolute blood pressure or blood pressure changes. Thechest strap of the device may include two electrodes, one on the leftand one on the right side of the chest. The electrodes may transmit acurrent through the chest and measure impedance. The device maysimultaneously measure the user's pulse via pulse oximetry. The devicemay then combine the ECG data and finger pulse rate to calculate pulsewave transit time which may be used to estimate blood pressure. Thepulse oximetry could be performed by the camera or light sensor on amobile phone or other mobile device that is wirelessly connected to thedevice.

In further embodiments the system may include a blood-oxygen levelsensor for generating blood-oxygen level data of the user. The bloodoxygen level sensor may be coupled with the processor. The processor mayraise or lower the first threshold in response to the blood-oxygen leveldata from the blood oxygen level sensor.

In various implementations, the physiological parameters recorded by thesensors can include abdominal/body orientation, snoring, blood oxygen,blood pressure, location of center of gravity, activities like running,walking, driving, sleeping, body heat, altitude tracking, pressurereadings, temperature readings, and/or user pedometer readings,breathing during sleep, fetal activity monitoring (fetal kicks), fetalheart rate monitoring, uterine contraction monitoring, and fetal ECGmonitoring, respiration characteristics, tension, temperature (measuredat any variety of locations on or in the body), and/or hemodynamic flowrestriction. In various implementations, the system is configured toestablish various risk thresholds, which can be based on one or more ofthese physiological parameters.

Importantly, in certain implementations a “physiological parameter” caninclude various psychological states. While psychology can be consideredas an alternative to physiology in certain applications, as used hereinthe term “physiological parameter” is meant to encompass certainpsychological states, as is discussed further herein.

In various implementations, the device and system provides feedback tothe user. In some embodiments, the device may provide vibrational,visual, or audio feedback to the user based on past or currentorientation of their abdomen. Feedback could be used to give positivereinforcement of a good abdominal position, to suggest a specificposition, to give constructive criticism regarding a non-ideal position,or to reinforce compliance for bed rest. Additionally, the device couldbe configured to provide feedback only during certain times during theday.

In certain implementations, the device and system performs variousfunctions by way of a system of electronic computing components. In someembodiments, a processor may identify orientation risk values associatedwith the estimated orientations of the abdomen to produce a time seriesof identified orientation risk values. A first cumulative risk value maybe calculated and updated by calculating a first moving average for asubset of the time series of identified orientation risk valuesassociated with the estimated orientations of the abdomen. The subsetfor the first moving average may have a first size. The first size maybe at least the last 30 seconds of sensor data. In some embodiments, itmay be the last two minutes of sensor data. Thereafter the processor maycompare the first cumulative risk value to a first threshold and outputa warning when the first cumulative risk value crosses the firstthreshold. Optionally, an input may be coupled with the processor. Theinput may be configured to receive a user input of pregnancy factorscomprising at least one of a multiple pregnancy of the user, body massindex (“BMI”) of the user, prior live births of the user, andpreexisting hypertension of the user. The processor may raise or lowerthe first threshold in response to the user input of pregnancy factors.

The processor may be further configured to calculate and update a secondcumulative risk value by calculating a second moving average for asubset of the time series of identified orientation risk values. Thesubset for the second moving average may include at least the last 5seconds of sensor data. The processor may compare the second cumulativerisk value to a second threshold and output a warning when the secondcumulative risk value crosses the second threshold.

In some embodiments, the processor may be further configured to processthe sensor data for the applications listed above by calculating andupdating a moving average for a subset of the time series associatedwith that sensor. The processor may be further configured to compare thedata collected from the various sensors to a data set communicating thesensor outputs for the ideal person of similar gestational age as theuser. For example, the processor could receive force measurements from asensor, calculate force changes over time using the received forcemeasurements, and determining whether the user is engaged in clinicallysignificant activity based on the calculated force changes and anactivity threshold. The processor could then compare the clinicallysignificant activity to the recommended activity level or threshold fora person of the same gestational age.

In some embodiments the system may include an infrared sensor coupledwith the processor. The processor may determine device use in responseto infrared sensor data.

The processor may be further configured to combine the recorded timeseries of orientation risk values with the recorded time series ofactivity risk values to generate a continuous time series of riskvalues. A cumulative risk may be calculated on a subset of thecontinuous time series of risk values by calculating a moving averagefor a subset of the continuous time series of risk values. Thereafter,the processor may be configured to compare the cumulative risk to acumulative risk threshold value and output a warning when the cumulativerisk crosses the cumulative risk threshold value. In additionalembodiments, the risk value may be reported to the user without havingbeen compared to a threshold.

Optionally, the processing device, by executing the logic or algorithm,may be further configured to perform additional operations. Examples caninclude: when the user is determined to be engaged in activity,recording a cumulative time duration of the clinically significantactivity by the user over a period of time and comparing the cumulativetime duration of the clinically significant activity engaged by the userover the time period to a preferred cumulative activity threshold.

The preferred cumulative activity or condition threshold may bedependent on a pregnancy stage of the user.

The system may further include the sensor. The may be housed in a firsthousing and the processor may be housed in a second housing separatefrom the first housing. The sensor may be wirelessly coupled with theprocessor.

In further embodiments, a method for reducing the risk of preterm birthin women is provided. The method may include receiving sensor data froma sensor coupled with a user and determining whether the user is engagedactivity based on the received sensor data. When the user is determinedto not be engaged in activity, the method may include monitoring theorientation of the abdomen of the user by processing the sensor data toestimate the orientation of the abdomen of the user and identifyingorientation risk values associated with the estimated orientations ofthe abdomen to produce a time series of identified orientation riskvalues. Thereafter, the method may include calculating and updating acumulative risk value by calculating a first moving average for a subsetof the time series of identified orientation risk values associated withthe estimated orientations of the abdomen and comparing the cumulativerisk value to a first threshold and a second threshold. A first warningmay be outputted when the cumulative risk value crosses the firstthreshold and a second warning when the cumulative risk value crossesthe second threshold.

The method may further include, when the user is determined to beengaged in clinically significant activity, stopping the producing ofthe time series of identified orientation risk values and identifyingactivity risk values associated with the sensor data to produce a timeseries of identified activity risk values. Calculating and updating thecumulative risk score may be performed by combining the time series ofidentified activity risk values and previously identified orientationrisk values and calculating a moving average for a subset of thecombined time series of identified activity risk values and previouslyidentified orientation risk values.

In further embodiments, a wearable device system for reducing the riskof preterm birth in women is provided where the wearable device mayinclude one or more sensors for continuously generating force dataindicative of an activity of the user. A processor may be coupled withthe one or more sensors and be configured to continuously monitor theactivity of the user by processing the force data to identify forcechanges in the force data to estimate a vigorousness of the activity ofthe user. The processor may compare the identified force changes to aforce change threshold value to determine whether the user is engaged inclinically significant activity. When the user is engaged in clinicallysignificant activity, the processor may be configured to identifyactivity risk values associated with the identified force changes toproduce a time series of identified activity risk values. The processormay also calculate and update a cumulative risk value by calculating amoving average for a subset of the time series of identified activityrisk values associated with the identified force changes and compare thecumulative risk value to a threshold. A warning may be outputted whenthe first cumulative risk value crosses the first threshold.

The force changes may be calculated by identifying a difference betweena max force and a minimum force in the force data during a time intervaland the processor may produce a time series of calculated force changes.Optionally, the processor identifies clinically significant useractivity by: calculating and updating a user activity moving average fora subset of the time series of calculated force changes associated withthe user activity and comparing the user activity moving average to anactivity threshold to determine whether the user is engaged inclinically significant activity. The processor may further record acumulative time duration of the clinically significant activity engagedby the user over a period of time.

An input may be provided that is coupled with the processor andconfigured to receive user input of a gestational age of a pregnancy ofthe user. The processor may be further configured to compare thecumulative time duration of clinically significant activity engaged bythe user over the period of time to a preferred cumulative activitythreshold specific for the gestational age of the pregnancy of the user.

In one embodiment, the device is used to train the user whichorientations and activities are preferable so that the device does notneed to be worn through the entire course of pregnancy. The device maybe used initially for a few hours or days or may be used periodicallythroughout pregnancy to refresh the user's memory as to which activitiesand orientations are preferred. The reminders can come in the form ofalerts, text messages, alarms, or any other known form of reminder tothe user. In a further embodiment, the training device comprises an appon a mobile device which is attached to the user's torso.

In further embodiments, a device may be provided for inhibitingpreeclampsia of a woman. The device may include a sensor configured togenerating orientation data and a support configured to couple thesensor to an abdomen of the woman such that the orientation data isindicative of an orientation of the abdomen. A processor may be coupledto the sensor so that it receives the orientation data. The processormay be configured to calculate a time series of values in response tothe data and a cumulative risk value in response to the calculatedvalues. The processor may have an output for transmitting a warning inresponse to the risk value such that preeclampsia risk is mitigated.

Exemplary implementations of the system relate to monitoring abdominalhemodynamics and preventing inferior vena cava (“IVC”) compression.Placental health is vital to a healthy full-term pregnancy; however,there is growing evidence that regular compression of the IVC by thegravid uterus causes periods of significant placental and fetal hypoxia.These events slowly contribute to pathologic compensatory reactions bythe body, including inflammation of the vasculature of the placenta,which accelerate the onset of hypertension and preeclampsia and alsoincrease the prevalence of gestational diabetes, IUGR, and stillbirth.Some embodiments prevent fetal hypoxia by limiting the amount of timethat blood flow is restricted to the placenta and fetus. Since fetalhypoxia results in children with decreased IQ and increased likelihoodof learning disabilities, some embodiments could improve cognitiveabilities of children.

Some obstetricians incorrectly believe that the supine position must notbe unhealthy or it would have been selected out in evolutionaryprocesses, or that a woman's body will “tell her” if she needs to move.There are a number of reasons why these arguments are specious. First,there is the direct evidence that supine sleep results in higher ratesof IUGR and stillbirth. Secondly, in 2015, 303,000 women died as adirect result of pregnancy or childbirth, and 99% of these were in thedeveloping world. Clearly even 1000s of years of human evolution areunable to perfect pregnancy without modern medical interventions.Pregnancy is a very delicate balance between the needs of the mother andthe incredible oxygen and nutrient requirements of the developingplacenta and fetus. Occluding abdominal blood flow for significantperiods of time each day or night can absolutely tip the balance andlead to downstream complications. Not surprisingly, uterine artery bloodflow drops an average of 34% in pregnant women when in the supineposition. Also, not all women feel a natural compulsion to change bodyorientations when flow is occluded in their IVC, especially when theyare deep in sleep.

In various embodiments, the system uses sensors to determine bodyorientation and blood oxygen levels. It then approximates the degree ofvenous compression occurring and generates vibrational alerts to theuser when she may benefit from changing positions or activities. Duringthe night, it delivers low level vibrations that do not wake users butsubconsciously encourages them to shift positions. The device capturessensor data as well as user reactions to the alerts which can bereviewed by the user's obstetrician. In addition, the device can be usedwith the alerts turned off entirely, and instead, just provide userswith a summary of their sleep behavior the next morning. This educationalone is very important as body sleep orientation often dramaticallychanges over the course of pregnancy and studies show that over 60% ofall pregnant women that sleep on their backs for significant periods oftime each night don't realize that they do so. The device can also usespecific user characteristics (eg BMI, singlet or twins, chronichypertension, diagnostic blood tests, etc.) as well as gestational ageto adjust risk threshold levels used in generating alerts.

Exemplary implementations of the system relate to diagnosing andpreventing Sleep Disordered Breathing (“SDB”). Sleep disorderedbreathing is another major impediment to healthy placental growth. Sleepapnea and heavy snoring have both been shown to dramatically lowersystemic blood oxygen concentrations in pregnant and non-pregnantindividuals. Sleep disordered breathing is a known contributor tohypertension in the general population, so it is not surprising thatnumerous studies have shown that snoring and sleep apnea increase therisk of preeclampsia, gestational diabetes, and premature birth

One of the greatest challenges in identifying SDB in pregnancy is itsdynamic nature. Prevalence of snoring and sleep apnea during pregnancyincrease by 200-300% compared to pre-pregnancy levels. By definition,the sleep breathing pathology in most pregnant women does not exist atall in early pregnancy, but it does exist, often in severe forms, inlate pregnancy. As such, there is no way to know exactly when it becomesa clinically relevant issue. Standard single night sleep monitorsfrequently miss SDB pathology entirely simply because of the variabilityin SDB over the course of pregnancy. These monitors cannot be worn nightafter night simply because they are so uncomfortable and complicated.The monitoring system, however, is practically invisible to the user yetis able to assess the daily or weekly progression of SDB to moreaccurately determine which women are at serious risk and may needfurther therapies such as a CPAP device. In addition, since themonitoring system trains women to avoid the supine position, it caneliminate the roughly half of all cases of SDB that are positionalrelated.

Some existing home-use wearable pregnancy devices monitor metrics suchas fetal heart rate and uterine contractions. Although these data mayimprove resultant care in a small number of cases, they typically resultin urgent reactionary medicine in the last month of gestation, andfrequently generate false positive alerts. In contrast, the presentsystem was developed on the premise that there are a large number ofuser physiologies and behaviors that have profound impacts on pregnancyoutcomes that can be monitored and improved throughout pregnancy.

Of particular interest to us are the many complications of pregnancywhich cannot simply be fixed once they manifest. Despite decades ofresearch into the etiology of preeclampsia, its exact pathogenesisremains uncertain; however, it is becoming increasingly apparent thatpreeclampsia is directly related to underlying placental pathology anddysfunction that develop over many months. Therefore, it inherently isnot a disease that can be fixed at the end of pregnancy, it is one thatmust be prevented.

As most cases of preeclampsia occur at the end of pregnancy, the bestway to prevent the majority of cases is to delay onset of dangerouslyhigh blood pressure until the pregnancy is full-term. The presentlydisclosed system tracks the adjustable factors in pregnancy that can,prevent diseases and negative conditions. By way of example, theseadjustable factors include the following. Sleep disordered breathingleads to hypertension in both pregnant and non-pregnant individuals.Similarly, snoring and sleep apnea are highly correlated to hypertensivediseases of pregnancy such as preeclampsia, as well as gestationaldiabetes and premature birth. Uterine artery blood flow and fetaloxygenation are both shown to significantly decrease in the supineposition. Numerous studies have now shown that women who spendsignificant time in reclined and supine positions have much higher ratesof stillbirth and intrauterine growth restriction. Depression and stressdouble the risk of preterm birth even after covariants such as loweconomic status and drug use are excluded. This is not surprising asdepression is known to cause hypertension and increase placental andfetal cortisol levels.

The placenta serves as the lungs, kidneys, liver, endocrine system, andimmune defense of the fetus. Despite its enormous importance, theplacenta is arguably the least understood of all human organs. This ispartly because the development of the human placenta cannot beinvasively studied because it only exists during pregnancy at which timeit is performing life-maintaining duties for the fetus. Notsurprisingly, as we learn more about this mysterious organ, we alsolearn of its great importance in protecting the fetus from potentialhazards during the pregnancy.

Exemplary implementations of the system relate to monitoring abdominalhemodynamics and preventing inferior vena cava (“IVC”) compression.Placental health is vital to a healthy full-term pregnancy; however,there is growing evidence that regular compression of the inferior venacava (IVC) by the gravid uterus causes periods of significant placentaland fetal hypoxia. These events slowly contribute to pathologiccompensatory reactions by the body, including inflammation of thevasculature of the placenta, which accelerate the onset of hypertensionand preeclampsia and also increase the prevalence of gestationaldiabetes, IUGR, and stillbirth.

Some obstetricians incorrectly believe that the supine position must notbe unhealthy or it would have been selected out in evolutionaryprocesses, or that a woman's body will “tell her” if she needs to move.There are a number of reasons why these arguments are specious. First,there is the direct evidence that supine sleep results in higher ratesof IUGR and stillbirth. Secondly, in 2015, 303,000 women died as adirect result of pregnancy or childbirth, and 99% of these were in thedeveloping world. Clearly even 1000s of years of human evolution areunable to perfect pregnancy without modern medical interventions.Pregnancy is a very delicate balance between the needs of the mother andthe incredible oxygen and nutrient requirements of the developingplacenta and fetus. Occluding abdominal blood flow for significantperiods of time each day or night can absolutely tip the balance andlead to downstream complications. Not surprisingly, uterine artery bloodflow drops an average of 34% in pregnant women when in the supineposition. Also, not all women feel a natural compulsion to change bodyorientations when flow is occluded in their IVC, especially when theyare deep in sleep.

In various embodiments, the system uses sensors to determine bodyorientation and blood oxygen levels. It then approximates the degree ofvenous compression occurring and generates vibrational alerts to theuser when she may benefit from changing positions or activities. Duringthe night, it delivers low level vibrations that do not wake users butsubconsciously encourages them to shift positions. The device capturessensor data as well as user reactions to the alerts which can bereviewed by the user's obstetrician. In addition, the device can be usedwith the alerts turned off entirely, and instead, just provide userswith a summary of their sleep behavior the next morning. This educationalone is very important as body sleep orientation often dramaticallychanges over the course of pregnancy and studies show that over 60% ofall pregnant women that sleep on their backs for significant periods oftime each night don't realize that they do so. The device can also usespecific user characteristics (e.g. BMI, singlet or twins, chronichypertension, diagnostic blood tests, etc.) as well as gestational ageto adjust risk threshold levels used in generating alerts.

Exemplary implementations of the system relate to diagnosing andpreventing Sleep Disordered Breathing (“SDB”). Sleep disorderedbreathing is another major impediment to healthy placental growth. Sleepapnea and heavy snoring have both been shown to dramatically lowersystemic blood oxygen concentrations in pregnant and non-pregnantindividuals. Sleep disordered breathing is a known contributor tohypertension in the general population, so it is not surprising thatnumerous studies have shown that snoring and sleep apnea increase therisk of preeclampsia, gestational diabetes, and premature birth.

One of the greatest challenges in identifying SDB in pregnancy is itsdynamic nature. Prevalence of snoring and sleep apnea during pregnancyincrease by 200-300% compared to pre-pregnancy levels. By definition,the sleep breathing pathology in most pregnant women does not exist atall in early pregnancy, but it does exist, often in severe forms, inlate pregnancy. As such, there is no way to know exactly when it becomesa clinically relevant issue. Standard single night sleep monitorsfrequently miss SDB pathology entirely simply because of the variabilityin SDB over the course of pregnancy. These monitors cannot be worn nightafter night simply because they are so uncomfortable and complicated.The Monitoring system, however, is practically invisible to the user yetis able to assess the daily or weekly progression of SDB to moreaccurately determine which women are at serious risk and may needfurther therapies such as a CPAP device. In addition, since themonitoring system trains women to avoid the supine position, it caneliminate the roughly half of all cases of SDB that are positionalrelated.

In one embodiment, the system is a health monitoring system with one ormore sensors for generating sensor data, configured to identify healthrisk values associated with the sensor data; assign the risk value to acorresponding time value; and output a warning when the risk valuepersists for the direction of the time value.

In one embodiment, the system is a health monitoring system with one ormore sensors for generating sensor data, configured to identify healthrisk values associated with the sensor data; assign the risk value to acorresponding time value; adjust the time values continuously based onnew risk values; and output a warning when the risk value persists forthe direction of the time value.

An example of this approach is the following: every body orientation hasa set amount of time in which the user is allowed to be before an alertis issued. In one embodiment, every single unique combination of sidetilt and recline angles has a different total amount of time associatedwith it. In one embodiment, a recline angle of −90 degrees with 0degrees side tilt (flat on back) has the very lowest amount of timeallowed (2 minutes). A combination of recline angle −75 degrees and sidetilt 7 degrees has a maximum time of 4 minutes and 22 seconds. Acombination of −35 recline and −82 side tilt has no maximum time value(infinity) because it is considered completely safe. Every time the usermoves orientations, the algorithm instantly calculates how much time theuser has left in that position. The remaining time is transferred andprorated to the new position. So, if the user has been flat on her backfor 1 of the maximum 2 minutes allowed by the algorithm and then shiftsto recline −75 and side tilt 7, she will have 50% of the time left forthe new position (2 min and 11 seconds).

In various embodiments, the system defines zones and assigns risk scoresbased on the user's presence in those zones compared with definedthresholds. In various embodiments, these zones can represent variousorientations, positions and the like, as related to the physiologicalparameters discussed elsewhere herein.

In another embodiment, the risk and time values are based on continuousequations so there are an infinite number of risk and time levels. Inone embodiment, different risk equations are used to cover differentquadrants of space.

In another embodiment, more than 1 real time risk variable is used inthe risk calculation equation. In one embodiment, the overall risk is adirect function of both body orientation and snoring so that the alertis more likely to go off when the user has high risk values for each ofbody orientation and snoring.

While some embodiments are generally discussed in terms of monitoringorientation and activity of pregnant women, many methods, devices, andsystems may be applied to any other area of the body where differentpositions have different levels of risk or benefit associated with them.For example, users with obesity related hypertension may benefit frompreventing abdominal fat from chronically or periodically compressingrenal nerves and abdominal veins which may increase hypertension. Inanother example, in a user with back pain, the algorithm assignsdifferent levels of risk or detriment to different positions. Thecumulative negative impact over time that the user experiences while invarious positions is added up and compared to allowable risk-time levelsto determine whether the user should be alerted to change position inorder to prevent back pain or muscle or nerve inflammation. Devices andmethods may be beneficial to users suffering from gastroesophahaelreflux or other digestive disorder that require they spend time incertain positions. Other diseases that may benefit from embodimentsdisclosed herein include Chorea, Parkinson's, and heart disease.

In one embodiment, an obese man may benefit from the positionalfunctionality of devices and systems described in this patentapplication. While sleeping, the device may issue vibrational alerts tothe man when he has been deemed to have been in one or more riskypositions for too long. In one embodiment, he has hypoventilationsyndrome and the mass of his abdomen makes it more difficult to breatheat night when in the supine position.

One or more computing devices may be adapted to provide desiredfunctionality by accessing software instructions rendered in acomputer-readable form. When software is used, any suitable programming,scripting, or other type of language or combinations of languages may beused to implement the teachings contained herein. However, software neednot be used exclusively, or at all. For example, some embodiments of themethods and systems set forth herein may also be implemented byhard-wired logic or other circuitry, including but not limited toapplication-specific circuits. Combinations of computer-executedsoftware and hard-wired logic or other circuitry may be suitable aswell.

In some embodiments, temperature of the user is measured in order todetermine the ideal fertility window for conception. In furtherembodiments, an algorithm estimates how much the core temperature of theuser has risen due to exercise or general activity. In some embodiments,the algorithm gets smarter over time by calibrating vigorousness ofactivity with temperature rise. In some embodiments, the temperature ismeasured directly by the device using sensors. In some embodiments,these sensors are on the skin, under the armpit, in the ear,intravaginal, or other places of the body known to provide consistenttemperature measurements. In some embodiments, a temperature sensor wornon the outside of the body measures ambient air temperature and anothersensor on the skin measures skin temperature. Further, in someembodiments, an algorithm account for the cooling or warming effect theenvironment has on skin temperature in order to estimate a more accuratebody temperature.

Embodiments of the methods disclosed herein may be executed by one ormore suitable computing devices. Such system(s) may comprise one or morecomputing devices adapted to perform one or more embodiments of themethods disclosed herein. As noted above, such devices may access one ormore computer-readable media that embody computer-readable instructionswhich, when executed by at least one computer, cause the at least onecomputer to implement one or more embodiments of the methods of thepresent subject matter. Additionally or alternatively, the computingdevice(s) may comprise circuitry that renders the device(s) operative toimplement one or more of the methods of the present subject matter.

Any suitable computer-readable medium or media may be used to implementor practice the presently-disclosed subject matter, including but notlimited to, diskettes, drives, and other magnetic-based storage media,optical storage media, including disks (e.g., CD-ROMS, DVD-ROMS,variants thereof, etc.), flash, RAM, ROM, and other memory devices, andthe like. Different arrangements of the components depicted in thedrawings or described above, as well as components and steps not shownor described are possible. Similarly, some features and sub-combinationsare useful and may be employed without reference to other features andsub-combinations. Embodiments of the system have been described forillustrative and not restrictive purposes, and alternative embodimentswill become apparent to readers of this patent. Accordingly, the variousimplementations of system are not limited to the embodiments describedabove or depicted in the drawings, and various embodiments andmodifications may be made without departing from the scope of the claimsbelow.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect includes a system for monitoring body orientation; wheredifferent levels of risk are assigned to specific discrete ranges ofbody orientations. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One general aspect includes a system for monitoring body orientation;where different levels of risk are assigned to specific discrete rangesof body orientations; where a user is allocated a maximum amount of timein any given orientation range before the user is prompted to move intoa less risky orientation. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One general aspect includes the system where a subset of bodyorientation ranges which are not risky have no maximum time limit. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

One general aspect includes the system in previous claims, where themaximum time allowed in a given range is affected by the duration oftime spent previously in other ranges. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One general aspect includes a system that includes a virtual model ofthe abdomen to calculate risk levels associated with different bodyorientations. The system also includes the system of the previous claim,where the abdominal model estimates hemodynamic characteristics of oneor more arteries and veins of the uterus, kidneys, legs, liver,placenta, and fetus. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One general aspect includes a system for predicting or diagnosingpreeclampsia, including: one or more acoustic sensors, and a processorconfigured to analyze sensor data. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. In oneExample, A system for reducing fetal risk in a user, including: awearable device; a memory unit; at least one sensor configured tomonitor the user for at least one physiological parameter and generatesensor data; and a processor coupled to the at least one sensor andconfigured to: monitor fetal risk by processing sensor data to identifyrisk values associated with the at least one physiological parameter toproduce a time series of identified fetal risk values; and calculate andupdate a first cumulative risk value by calculating a first movingaverage for a subset of the time series of identified fetal risk values.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Thesystem where the processor is configured to compare the at least onecumulative risk value to a risk threshold. The system where the deviceis configured to output a warning when the first cumulative risk valueexceeds the first threshold. The system further including a database incommunication with the processor, where: the database includes at leastone adapting risk threshold and is configured to adapt the at least oneadapting risk threshold based on the recorded time series of identifiedfetal risk values, and the processor is configured to compare the atleast one cumulative risk value to the at least one adapting riskthreshold. The system where at least one sensor is a non-orientationsensor configured to monitor a non-orientation physiological parameterand the processor is configured to assign at least one non-orientationfetal risk value. The system where the at least one sensor is selectedfrom the group including: an acoustic sensor, a blood oxygen sensor, aphoto diode, an accelerometer, an orientation sensor, a pressure sensor,and an ultra wide band sensor. The system where the physiologicalparameter is selected from the group including: blood oxygenconcentration, heart rate, sleep position, sleep quality, respirationcharacteristics, snoring, sleep apnea, hypoventilation, and/orhyperventilation and combinations thereof. The system further includinga garment configured to house the device and retain a fixed orientationrelative to the user. The system where the garment is selected from thegroup including a shirt and an undergarment. The system furtherincluding at least one magnet configured to promote device orientation.The system further including a clip configured to promote deviceorientation. The system where the at least one sensor is selected fromthe group including an acoustic sensor, an accelerometer, an ultra wideband sensor, a blood oxygen sensor and a photo sensor. The system wherethe system is configured to alert the user when the identified fetalrisk values exceed an established risk threshold. The system furtherincluding an external sensor disposed outside the housing. The systemfurther including at least one fixed orientation promoter. The systemwhere the fixed orientation promoter is selected from the groupincluding a garment, a magnet and a clip. The device further including agarment selected from the group including: a shirt, a belt, orunderpants. The system further including an alert system. The systemwhere the processor is configured to generate and compare at least onecumulative risk value from the fetal risk scores for comparison to arisk threshold. The health monitoring and improvement system where thehealth monitoring and improvement system is used to monitor and improvethe health of the user during pregnancy. The health monitoring andimprovement system where the health monitoring and improvement system isused to monitor and improve the health of a fetus during the pregnancyterm. The health monitoring and improvement system where the healthmonitoring and improvement system is used to monitor and improve thehealth of obese or other clinically high risk individuals. The datacollection system where the data collection system is included of one ormore sensors. The data collection system where the data collectionsystem is included of one or more sensors selected from the groupincluding: accelerometers, gyroscopes, magnetometers, infrared,temperature, pressure, microphone, oximeters, ultra wide band microwave,and radio. The data collection system where one or more sensors aresingle axis sensors. The data collection system where one or moresensors are 3-axis sensors. The data collection system where the sensorsare user-programmable. The data collection system where the datacollection system contains analog-to-digital converters for digitizingthe output from one or more sensors. The data collection system wherethe data collection system collects data regarding physiologicalparameters specific to the user. The data collection system where thephysiological parameters are selected from the group including:abdominal/body orientation, snoring, blood oxygen, blood pressure,location of center of gravity, physical activity, body heat, altitudetracking, pressure, temperature, respiration, respiration during sleep,fetal activity, fetal heart rate, uterine contraction, fetal ECG,tension, and hemodynamic flow. The data collection system where thephysiological parameters include psychological states. The datacollection system where the data collection system uses empiricalreference data to determine force, impulse, or pressure applied tointra-abdomical tissues or organs. The data collection system where thetissues and organs are selected from the group including: spine,kidneys, liver, bladder, all abdominal blood vessels including theinferior vena cava, and all abdominal nerves. The device where thedevice further includes: a PCB; a battery; and a vibrational motor. Thedevice where the device is affixed to the body using adhesives. Thedevice where the device is affixed to the garment using magnets. Thedevice where the device is affixed to the body using straps. The devicewhere the device is affixed to the body using magnets. The processingsystem where the manual inputs are selected from the group including:number of pregnancies of the user, body mass index, prior live births,preexisting hypertension. The processing system where the processor mayraise or lower the threshold in response to the manual input selections.The processing system where the processing system is configured tocompare physiological parameters to the physiological parameters of anideal user. The processing system where the processor is configured toidentify clinically significant physiological parameter values. Theprocessing system where the processing system is configured tocommunicate with an external device. The processing system where thephysiological parameters also include psychological states. Theprocessing system where the external device is selected from the groupincluding: cellular, hand-held, or desktop. The processing system wherethe processing system is configured to communicate outputs to the useror the user's care specialist. The processing system where theprocessing system is configured to recognize appropriate time spans fordata processing. The device where the device is fixed internally in thebody The device where the device is attached to the user in a mannerthat prevents rotation. The processing system where the processingsystem identifies risk values associated with one or more physiologicalparameters. The processing system where the physiological parameters areselected from the group including: abdominal/body orientation, snoring,blood oxygen, blood pressure, location of center of gravity, physicalactivity, body heat, altitude tracking, pressure, temperature,respiration, respiration during sleep, fetal activity, fetal heart rate,uterine contraction, fetal ECG, tension, and hemodynamic flow. Theprocessing system where the processing system determines risk values bycreating a moving average of the physiological parameter data andcomparing it to a threshold. The processing system where the movingaverage calculated over any of a variety of time spans. The processingsystem where there is a minimum and maximum threshold limitation. Theprocessing system where the processor is coupled with manual inputs. Thefeedback system where the feedback system informs the user when a riskvalue exceeds the threshold value for a physiological parameter. Thefeedback system where the physiological parameters are selected from thegroup including: abdominal/body orientation, snoring, blood oxygen,blood pressure, location of center of gravity, physical activity, bodyheat, altitude tracking, pressure, temperature, respiration, respirationduring sleep, fetal activity, fetal heart rate, uterine contraction,fetal ECG, tension, and hemodynamic flow. The feedback system where thephysiological parameters also include psychological states. The feedbacksystem where the feedback is selected from the group including lightindicators, display schemes, audio, vibrational. The feedback systemwhere the feedback system informs the user when a physiologicalparameter is inside the minimum and maximum threshold values. Thefeedback system where the feedback system informs the user when the timeaverage of a physiological parameter is exceeds the threshold values.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

In one Example, A system for reducing fetal risk in a user, including: afetal risk device including a housing; a transmission component disposedin the housing; at least one sensor disposed within the housing formonitoring a physiological parameter and generating parameter data; aprocessor configured to assign fetal risk values from the parameter datato produce a time series of identified fetal risk values; and an alertsystem configured to notify the user of identified risk values. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Thesystem where the at least one sensor is selected from the groupincluding an acoustic sensor, an accelerometer, an ultra wide bandsensor, a blood oxygen sensor and a photo sensor. The system where thesystem is configured to alert the user when the identified fetal riskvalues exceed an established risk threshold. The system furtherincluding an external sensor disposed outside the housing. The systemfurther including at least one fixed orientation promoter. The systemwhere the fixed orientation promoter is selected from the groupincluding a garment, a magnet and a clip. The device further including agarment selected from the group including: a shirt, a belt, orunderpants. The system further including an alert system. The systemwhere the processor is configured to generate and compare at least onecumulative risk value from the fetal risk scores for comparison to arisk threshold. The health monitoring and improvement system where thehealth monitoring and improvement system is used to monitor and improvethe health of the user during pregnancy. The health monitoring andimprovement system where the health monitoring and improvement system isused to monitor and improve the health of a fetus during the pregnancyterm. The health monitoring and improvement system where the healthmonitoring and improvement system is used to monitor and improve thehealth of obese or other clinically high risk individuals. The datacollection system where the data collection system is included of one ormore sensors. The data collection system where the data collectionsystem is included of one or more sensors selected from the groupincluding: accelerometers, gyroscopes, magnetometers, infrared,temperature, pressure, microphone, oximeters, ultra wide band microwave,and radio. The data collection system where one or more sensors aresingle axis sensors. The data collection system where one or moresensors are 3-axis sensors. The data collection system where the sensorsare user-programmable. The data collection system where the datacollection system contains analog-to-digital converters for digitizingthe output from one or more sensors. The data collection system wherethe data collection system collects data regarding physiologicalparameters specific to the user. The data collection system where thephysiological parameters are selected from the group including:abdominal/body orientation, snoring, blood oxygen, blood pressure,location of center of gravity, physical activity, body heat, altitudetracking, pressure, temperature, respiration, respiration during sleep,fetal activity, fetal heart rate, uterine contraction, fetal ECG,tension, and hemodynamic flow. The data collection system where thephysiological parameters include psychological states. The datacollection system where the data collection system uses empiricalreference data to determine force, impulse, or pressure applied tointra-abdomical tissues or organs. The data collection system where thetissues and organs are selected from the group including: spine,kidneys, liver, bladder, all abdominal blood vessels including theinferior vena cava, and all abdominal nerves. The device where thedevice further includes: a PCB; a battery; and a vibrational motor. Thedevice where the device is affixed to the body using adhesives. Thedevice where the device is affixed to the garment using magnets. Thedevice where the device is affixed to the body using straps. The devicewhere the device is affixed to the body using magnets. The processingsystem where the manual inputs are selected from the group including:number of pregnancies of the user, body mass index, prior live births,preexisting hypertension. The processing system where the processor mayraise or lower the threshold in response to the manual input selections.The processing system where the processing system is configured tocompare physiological parameters to the physiological parameters of anideal user. The processing system where the processor is configured toidentify clinically significant physiological parameter values. Theprocessing system where the processing system is configured tocommunicate with an external device. The processing system where thephysiological parameters also include psychological states. Theprocessing system where the external device is selected from the groupincluding: cellular, hand-held, or desktop. The processing system wherethe processing system is configured to communicate outputs to the useror the user's care specialist. The processing system where theprocessing system is configured to recognize appropriate time spans fordata processing. The device where the device is fixed internally in thebody The device where the device is attached to the user in a mannerthat prevents rotation. The processing system where the processingsystem identifies risk values associated with one or more physiologicalparameters. The processing system where the physiological parameters areselected from the group including: abdominal/body orientation, snoring,blood oxygen, blood pressure, location of center of gravity, physicalactivity, body heat, altitude tracking, pressure, temperature,respiration, respiration during sleep, fetal activity, fetal heart rate,uterine contraction, fetal ECG, tension, and hemodynamic flow. Theprocessing system where the processing system determines risk values bycreating a moving average of the physiological parameter data andcomparing it to a threshold. The processing system where the movingaverage calculated over any of a variety of time spans. The processingsystem where there is a minimum and maximum threshold limitation. Theprocessing system where the processor is coupled with manual inputs. Thefeedback system where the feedback system informs the user when a riskvalue exceeds the threshold value for a physiological parameter. Thefeedback system where the physiological parameters are selected from thegroup including: abdominal/body orientation, snoring, blood oxygen,blood pressure, location of center of gravity, physical activity, bodyheat, altitude tracking, pressure, temperature, respiration, respirationduring sleep, fetal activity, fetal heart rate, uterine contraction,fetal ECG, tension, and hemodynamic flow. The feedback system where thephysiological parameters also include psychological states. The feedbacksystem where the feedback is selected from the group including lightindicators, display schemes, audio, vibrational. The feedback systemwhere the feedback system informs the user when a physiologicalparameter is inside the minimum and maximum threshold values. Thefeedback system where the feedback system informs the user when the timeaverage of a physiological parameter is exceeds the threshold values.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

In one Example, A system for reducing fetal risk in a user, including: awearable device housing including a housing, a memory unit and atransmission unit; at least one sensor disposed within the housing andconfigured to generate fetal risk data relating to physiologicalparameters; and a processor coupled to the sensor so as to receive thefetal risk data, where the processor is configured to calculate andproduce a time-series of fetal risk scores in response to the fetal riskdata, and where the fetal risk scores can be wirelessly transmitted viathe transmission unit. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Thesystem further including an alert system. The system where the processoris configured to generate and compare at least one cumulative risk valuefrom the fetal risk scores for comparison to a risk threshold. Thehealth monitoring and improvement system where the health monitoring andimprovement system is used to monitor and improve the health of the userduring pregnancy. The health monitoring and improvement system where thehealth monitoring and improvement system is used to monitor and improvethe health of a fetus during the pregnancy term. The health monitoringand improvement system where the health monitoring and improvementsystem is used to monitor and improve the health of obese or otherclinically high risk individuals. The data collection system where thedata collection system is included of one or more sensors. The datacollection system where the data collection system is included of one ormore sensors selected from the group including: accelerometers,gyroscopes, magnetometers, infrared, temperature, pressure, microphone,oximeters, ultra wide band microwave, and radio. The data collectionsystem where one or more sensors are single axis sensors. The datacollection system where one or more sensors are 3-axis sensors. The datacollection system where the sensors are user-programmable. The datacollection system where the data collection system containsanalog-to-digital converters for digitizing the output from one or moresensors. The data collection system where the data collection systemcollects data regarding physiological parameters specific to the user.The data collection system where the physiological parameters areselected from the group including: abdominal/body orientation, snoring,blood oxygen, blood pressure, location of center of gravity, physicalactivity, body heat, altitude tracking, pressure, temperature,respiration, respiration during sleep, fetal activity, fetal heart rate,uterine contraction, fetal ECG, tension, and hemodynamic flow. The datacollection system where the physiological parameters includepsychological states. The data collection system where the datacollection system uses empirical reference data to determine force,impulse, or pressure applied to intra-abdomical tissues or organs. Thedata collection system where the tissues and organs are selected fromthe group including: spine, kidneys, liver, bladder, all abdominal bloodvessels including the inferior vena cava, and all abdominal nerves. Thedevice where the device further includes: a PCB; a battery; and avibrational motor. The device where the device is affixed to the bodyusing adhesives. The device where the device is affixed to the garmentusing magnets. The device where the device is affixed to the body usingstraps. The device where the device is affixed to the body usingmagnets. The processing system where the manual inputs are selected fromthe group including: number of pregnancies of the user, body mass index,prior live births, preexisting hypertension. The processing system wherethe processor may raise or lower the threshold in response to the manualinput selections. The processing system where the processing system isconfigured to compare physiological parameters to the physiologicalparameters of an ideal user. The processing system where the processoris configured to identify clinically significant physiological parametervalues. The processing system where the processing system is configuredto communicate with an external device. The processing system where thephysiological parameters also include psychological states. Theprocessing system where the external device is selected from the groupincluding: cellular, hand-held, or desktop. The processing system wherethe processing system is configured to communicate outputs to the useror the user's care specialist. The processing system where theprocessing system is configured to recognize appropriate time spans fordata processing. The device where the device is fixed internally in thebody The device where the device is attached to the user in a mannerthat prevents rotation. The processing system where the processingsystem identifies risk values associated with one or more physiologicalparameters. The processing system where the physiological parameters areselected from the group including: abdominal/body orientation, snoring,blood oxygen, blood pressure, location of center of gravity, physicalactivity, body heat, altitude tracking, pressure, temperature,respiration, respiration during sleep, fetal activity, fetal heart rate,uterine contraction, fetal ECG, tension, and hemodynamic flow. Theprocessing system where the processing system determines risk values bycreating a moving average of the physiological parameter data andcomparing it to a threshold. The processing system where the movingaverage calculated over any of a variety of time spans. The processingsystem where there is a minimum and maximum threshold limitation. Theprocessing system where the processor is coupled with manual inputs. Thefeedback system where the feedback system informs the user when a riskvalue exceeds the threshold value for a physiological parameter. Thefeedback system where the physiological parameters are selected from thegroup including: abdominal/body orientation, snoring, blood oxygen,blood pressure, location of center of gravity, physical activity, bodyheat, altitude tracking, pressure, temperature, respiration, respirationduring sleep, fetal activity, fetal heart rate, uterine contraction,fetal ECG, tension, and hemodynamic flow. The feedback system where thephysiological parameters also include psychological states. The feedbacksystem where the feedback is selected from the group including lightindicators, display schemes, audio, vibrational. The feedback systemwhere the feedback system informs the user when a physiologicalparameter is inside the minimum and maximum threshold values. Thefeedback system where the feedback system informs the user when the timeaverage of a physiological parameter is exceeds the threshold values.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

In one Example, A health monitoring and improvement system, including: adevice; a data collection system; a processor, where the processingsystem collects sensor data from the device; and a feedback system.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Thehealth monitoring and improvement system where the health monitoring andimprovement system is used to monitor and improve the health of the userduring pregnancy. The health monitoring and improvement system where thehealth monitoring and improvement system is used to monitor and improvethe health of a fetus during the pregnancy term. The health monitoringand improvement system where the health monitoring and improvementsystem is used to monitor and improve the health of obese or otherclinically high risk individuals. The data collection system where thedata collection system is included of one or more sensors. The datacollection system where the data collection system is included of one ormore sensors selected from the group including: accelerometers,gyroscopes, magnetometers, infrared, temperature, pressure, microphone,oximeters, ultra wide band microwave, and radio. The data collectionsystem where one or more sensors are single axis sensors. The datacollection system where one or more sensors are 3-axis sensors. The datacollection system where the sensors are user-programmable. The datacollection system where the data collection system containsanalog-to-digital converters for digitizing the output from one or moresensors. The data collection system where the data collection systemcollects data regarding physiological parameters specific to the user.The data collection system where the physiological parameters areselected from the group including: abdominal/body orientation, snoring,blood oxygen, blood pressure, location of center of gravity, physicalactivity, body heat, altitude tracking, pressure, temperature,respiration, respiration during sleep, fetal activity, fetal heart rate,uterine contraction, fetal ECG, tension, and hemodynamic flow. The datacollection system where the physiological parameters includepsychological states. The data collection system where the datacollection system uses empirical reference data to determine force,impulse, or pressure applied to intra-abdomical tissues or organs. Thedata collection system where the tissues and organs are selected fromthe group including: spine, kidneys, liver, bladder, all abdominal bloodvessels including the inferior vena cava, and all abdominal nerves. Thedevice where the device further includes: a PCB; a battery; and avibrational motor. The device where the device is affixed to the bodyusing adhesives. The device where the device is affixed to the garmentusing magnets. The device where the device is affixed to the body usingstraps. The device where the device is affixed to the body usingmagnets. The processing system where the manual inputs are selected fromthe group including: number of pregnancies of the user, body mass index,prior live births, preexisting hypertension. The processing system wherethe processor may raise or lower the threshold in response to the manualinput selections. The processing system where the processing system isconfigured to compare physiological parameters to the physiologicalparameters of an ideal user. The processing system where the processoris configured to identify clinically significant physiological parametervalues. The processing system where the processing system is configuredto communicate with an external device. The processing system where thephysiological parameters also include psychological states. Theprocessing system where the external device is selected from the groupincluding: cellular, hand-held, or desktop. The processing system wherethe processing system is configured to communicate outputs to the useror the user's care specialist. The processing system where theprocessing system is configured to recognize appropriate time spans fordata processing. The device where the device is fixed internally in thebody The device where the device is attached to the user in a mannerthat prevents rotation. The processing system where the processingsystem identifies risk values associated with one or more physiologicalparameters. The processing system where the physiological parameters areselected from the group including: abdominal/body orientation, snoring,blood oxygen, blood pressure, location of center of gravity, physicalactivity, body heat, altitude tracking, pressure, temperature,respiration, respiration during sleep, fetal activity, fetal heart rate,uterine contraction, fetal ECG, tension, and hemodynamic flow. Theprocessing system where the processing system determines risk values bycreating a moving average of the physiological parameter data andcomparing it to a threshold. The processing system where the movingaverage calculated over any of a variety of time spans. The processingsystem where there is a minimum and maximum threshold limitation. Theprocessing system where the processor is coupled with manual inputs. Thefeedback system where the feedback system informs the user when a riskvalue exceeds the threshold value for a physiological parameter. Thefeedback system where the physiological parameters are selected from thegroup including: abdominal/body orientation, snoring, blood oxygen,blood pressure, location of center of gravity, physical activity, bodyheat, altitude tracking, pressure, temperature, respiration, respirationduring sleep, fetal activity, fetal heart rate, uterine contraction,fetal ECG, tension, and hemodynamic flow. The feedback system where thephysiological parameters also include psychological states. The feedbacksystem where the feedback is selected from the group including lightindicators, display schemes, audio, vibrational. The feedback systemwhere the feedback system informs the user when a physiologicalparameter is inside the minimum and maximum threshold values. Thefeedback system where the feedback system informs the user when the timeaverage of a physiological parameter is exceeds the threshold values.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

In one example, a wearable device system for reducing risks associatedwith birth, the wearable device comprising one or more sensors forgenerating sensor data indicative of an orientation of an abdomen of auser, a processor coupled with the one or more sensors, the processorconfigured to monitor the orientation of the abdomen of the user byprocessing the sensor data to estimate the orientation of the abdomen ofthe user, assign a health zone to the orientation, assign total amountsof time allowed for each health zone where zones of healthierorientations allow longer amounts of time and output a warning when thetime threshold is exceeded. In some examples, the user can be in one ofthe zones for any amount of time without an alert being issued. In someexamples, there are a minimum of 3 total health zones possible. In someexamples, a prorated time calculation is used to determine how much timeis carried over to new zones.

While multiple embodiments are disclosed, still other embodiments of thedisclosure will become apparent to those skilled in the art from thefollowing detailed description, which shows and describes illustrativeembodiments of the disclosed apparatus, systems and methods. As will berealized, the disclosed apparatus, systems and methods are capable ofmodifications in various obvious aspects, all without departing from thespirit and scope of the disclosure. Accordingly, the drawings anddetailed description are to be regarded as illustrative in nature andnot restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a system diagram of an exemplary device for trackingabdominal orientation and/or user activity.

FIG. 1B is a schematic overview of a network implementation of thesystem, according to one exemplary embodiment.

FIG. 2A illustrates an exemplary wearable device for tracking abdominalorientation and/or user activity which attaches to a user by straps.

FIG. 2B illustrates an exemplary coordinate system for the user and thedevice.

FIG. 2C depicts an alternate implementation of the device embedded in agarment.

FIG. 2D illustrates an exemplary wearable device for tracking abdominalorientation for placement on the user in a fixed orientation.

FIG. 3A depicts an implementation of a garment configured to local thedevice on a user's body.

FIG. 3B depicts a further implementation of a garment, according toseveral embodiments, showing various device placements on or above theabdomen of the user.

FIG. 3C depicts a front view of an embodiment of the device placed in apocket, according to one implementation.

FIG. 3D depicts a further view of a garment showing several possibledevice placement implementations.

FIG. 3E depicts a perspective view of a pocket, according to oneembodiment.

FIG. 3F is a front view of an implementation of a garment and pocket,showing device insertion.

FIG. 3G is a three-quarters rear view of an implementation of a garmenthaving a support pad.

FIG. 3H is a front view of an alternate embodiment of a garment, havinga pocket with at least one magnet disposed within.

FIG. 3I is a front view of a further implementation having a device withmultiple magnetometers.

FIG. 3J illustrates the exemplary wearable device of FIG. 2A placed onand attached to a user at an exemplary location.

FIG. 3K illustrates an exemplary system for tracking abdominalorientation and activity including the exemplary wearable device of FIG.2A in combination with another exemplary wearable device for trackingabdominal orientation and activity (which may be identical to theexemplary wearable device illustrated in FIG. 2A).

FIG. 3L illustrates the exemplary wearable device of FIG. 2D placed onand attached to a user at an exemplary location.

FIG. 4A shows a mating piece, according to an exemplary embodiment.

FIG. 4B shows a wearable device, according to an exemplary embodiment.

FIG. 4C shows a wearable device paired with a mating piece, according toan exemplary embodiment.

FIG. 4D shows a mating device in perspective, according to an exemplaryembodiment.

FIG. 4E shows an example of how magnets could be placed in a matingpiece.

FIG. 4F shows an example of how magnets could be placed in a wearabledevice.

FIG. 4G shows an exemplary embodiment in which a metal clip mates withmagnets in a device.

FIG. 5A shows an open clasping mechanism, according to an exemplaryembodiment.

FIG. 5B shows a closed clasping mechanism, according to an exemplaryembodiment.

FIG. 5C shows a clasping mechanism, according to another exemplaryembodiment.

FIG. 5D shows a front/back planar view of a clasping mechanism,according to an exemplary embodiment.

FIG. 5E shows a perspective view of a clasping mechanism, according toan exemplary embodiment.

FIG. 5F shows another perspective view of a clasping mechanism,according to an exemplary embodiment.

FIG. 5G shows a side planar view of a clasping mechanism, according toan exemplary embodiment.

FIG. 5H shows another perspective view of a clasping mechanism,according to an exemplary embodiment.

FIG. 5I shows a side planar view of a clasping mechanism, according toan exemplary embodiment.

FIG. 5J shows a side planar view of a clasping mechanism, according toan exemplary embodiment.

FIG. 5K shows a slightly opened clipping mechanism, according to anexemplary embodiment.

FIG. 5L shows a closed clipping mechanism, according to an exemplaryembodiment.

FIG. 6A shows a schematic to demonstrate a system which measuresphysiological parameters, according to an exemplary embodiment.

FIG. 6B shows a schematic to demonstrate a system which measuresphysiological parameters, according to an exemplary embodiment.

FIG. 7A shows a pair of smart underpants, with a pocket which might holdsensors or devices.

FIG. 7B shows a pair of smart underpants, with multiple pockets whichmight hold sensors or devices.

FIG. 8 illustrates an exemplary operational flow according to someembodiments.

FIG. 9 illustrates an exemplary method for monitor orientation risks.

FIG. 10A illustrates exemplary orientation risk value matrices.

FIG. 10B illustrates further exemplary orientation risk value matrices.

FIG. 10C illustrates a graph of representative risk scores for a varietyof positions.

FIG. 11 illustrates an exemplary method for monitoring activity risks.

FIG. 12 shows exemplary risk factors and exemplary risk values forcustomizing an orientation and/or activity monitoring algorithmaccording to some embodiments.

FIG. 13 illustrates an exemplary user interface for orientation riskmonitoring according to some embodiments.

FIG. 14 illustrates an exemplary user interface for activity riskmonitoring.

FIG. 15 illustrates another exemplary method according to someembodiments.

FIG. 16 illustrates a table and plot illustrating the exemplaryrelationship between risk coefficients and gestational age.

FIG. 17 is a flow chart of one algorithm implementation.

FIG. 18 is a flow chart of another algorithm implementation.

FIG. 19 is a flow chart of a further algorithm implementation.

FIG. 20 is a flow chart of yet another algorithm implementation.

DETAILED DESCRIPTION

The disclosed devices, systems and methods relate to various systems,devices and methods for health monitoring, particularly in pregnantwomen. In exemplary implementations, the disclosed implementationsrelate to preventing and predicting certain diseases and conditions byproviding various devices, systems and methods directed at collectinginformation about the activity of the user, including relating tophysiological parameters. The subject matter of embodiments of thepresent disclosure is described here with specificity, but thisdescription is not necessarily intended to limit the scope of theclaims. While the above examples are generally discussed with referenceto avoiding preeclampsia and other complications of pregnancy, it shouldbe understood that embodiments disclosed herein may be applicable topreventing other conditions as well, such as pressure on abdominal veinsand organs can be harmful outside of pregnancy as well. The claimedsubject matter may be embodied in other ways, may include differentelements or steps, and may be used in conjunction with other existing orfuture technologies. This description should not be interpreted asimplying any particular order or arrangement among or between varioussteps or elements except when the order of individual steps orarrangement of elements is explicitly described.

In various embodiments, and as shown in FIG. 1A-7B, various devices andsystems are provided which are directed to the use of a device 10. Invarious implementations, this device 10 can be secured to the user'sbody via an external mounting device 28, such as by way of a garment 30or clip, so as to maintain a constant relative orientation. Further, invarious implementations, and as shown in FIGS. 8-20 , the device 10readings can be used to assess risk and aid in the prevention andprediction of diseases. Accordingly, systems, devices, and methods totrack physiological parameters and provide clinically relevant feedbackto the user and/or physician in order to prevent, avoid, or reversediseases or conditions that are related to abdominal position.

As discussed above in relation to FIG. 1A, in certain implementations, ahealth monitoring system 1 is provided. In these implementations, thesystem has a minimum of one of more sensors 14, a processor 12, a memoryunit 18, and an alert system or feedback device 22; wherein each of thesensors 14 is configured to detect a predetermined physiologicalparameter of a person (sensors discussed below). In use, the system maycomprise several optional steps used to monitor at least onephysiological parameter and assess the associated fetal risk.

In these implementations, the system 1 records a time series ofindividual parameter values into the memory unit 18. Further, theprocessor 12 analyzes a predetermined number of the most recentlyrecorded physiological parameters to determine if one or more of thedetected parameters is abnormal as determined over a set period of time.When at least one of the physiological parameters is determined to beabnormal, an alert is generated. Further, long-term trends can beanalyzed, such that recorded data is aggregated and the user's riskprofile is calibrated over time, as would be apparent to a skilledartisan.

FIG. 8 illustrates an exemplary operational flow 100 according to someembodiments of the system 1 device 10. Sensor data may be received 102.From the sensor data, a processor may determine if the sensor data isindicative of significant user activity 104. If the sensor data isindicative of significant user activity, the processor may furtheranalyze the sensor data to monitor user activity and determine activityrisk with an activity risk algorithm 106. Based on the monitoring 106,the processor may output feedback to a user 108. When the sensor data isindicative of a user not engaged in significant activity, the processormay analyze the sensor data to monitor user orientation and determineorientation risk with an orientation risk algorithm 110. Based on themonitoring 110, the processor may output feedback to a user 108.Additionally, in some embodiments, the processor may be configuredcombine a time series of orientation risk values obtained from algorithm110 and a time series of activity risk values obtained from algorithm106 to generate a continuous time series of risk values 111. Thecontinuous time series of risk values may then be used to determine adaily cumulative risk 112 over an extended time period (e.g., 24 hoursstarting and ending each day at 3 a.m.). The daily cumulative risk maybe a function of the risk values obtained from the monitoring 106 and/orthe monitoring 110. The processor may output feedback 108 per the dailycumulative risk determination 112. Further, in some embodiments, thecontinuous time series of risk values may be used to calculate acumulative risk score 113 which may then be compared to a cumulativerisk threshold as will be discussed further below.

In certain implementations, the device can be used for two or more ofthe following purposes: used in pre-pregnancy or early pregnancy as afitness device, used in mid and late pregnancy to monitor bodyorientation, sleep, and depression, used in late pregnancy to monitorfetal activity and uterine contractions, used after pregnancy again as afitness device, and used for monitoring SIDS in the new baby.

These risk scores 113 can be updated as data is collected, as would beapparent to one of skill in the art. Further, the risk score algorithmcan be revised as outcomes for additional users are collected, as wouldbe apparent to one of skill in the art. While generally illustrated withorientation monitoring algorithms, activity monitoring algorithms, dailyrisk algorithms, and cumulative risk algorithms, it should be understoodthat embodiments may have one, some, or all of the functionalitydescribed above. Many embodiments may implement all of the functions,but other embodiments may be configured to only monitor user activityrisk or only monitor orientation risks or other sub combinations offunctions.

FIG. 1A illustrates an exemplary device 10 according to some embodimentsof the system 1. The device 10 may include a microcontroller (processor)12 coupled with one or more sensors 14. The device 10 may be powered bya rechargeable battery 16. In one embodiment, the rechargeable battery16 may be an Li-ion battery. The battery 16 may be recharged via aUniversal Serial Bus (USB) port, mini-USB port, micro-USB port or thelike. In one embodiment, there a charging base 25 is provided. In theseembodiments, the charging base 25 can be plugged into the wall andconnects to internet via Wi-Fi or wire. Various embodiments of the baseinclude, but are not limited to, sending an low batter alert email ortext to the user, auto-resetting time keeping devices, relaying messagesvia a external storage location (for example the cloud), monitoringuser's physiological parameters for a location external to the body, orplaying status updates.

Data storage 18 may be provided to store computer software executable bythe microcontroller 12 and the received sensor data from the one or moresensors 14. The device 10 may further include a wireless interface 20for interfacing with smartphones, tablets, or other mobile devices. Forexample, in some embodiments, data may be stored on the device 10 andtransmitted for processing at a later time. Alternatively, the device 10may transmit the data in substantial real-time to a user's personalelectronics device for data processing. In some embodiments, thewireless interface 20 may be a Wi-Fi or Bluetooth wireless interface.

Device 10 may further include an audio/visual/tactile feedback device 22for outputting signals to a device user. LED status indicators 24 mayalso be provided. In certain implementations, the indicators 24 can bean LCD screen, or other screen known by those of skill, such as thoseregularly used in mobile devices.

The microcontroller 12 may be configured to receive and process thesensor data from the one or more sensors 14. In many embodiments, themicrocontroller 12 may be configured to monitor user activity toidentify risks associated with certain levels of activity to the user.In many embodiments, the microcontroller 12 may be configured to monitoruser orientation to identify risks associated with certain userorientations. Optionally, the microcontroller 12 may be configured totransmit the sensor data from the one or more sensors 14 to a processorhoused separately from the device 10 for data analysis at the separateprocessor. This may be beneficial when increased processing power isdesired and/or when reducing a footprint of device 10. The separateprocessor may be a portable electronic device of the user, a desktopcomputer, or a related technology.

As shown, certain embodiments provide for a wearable device forcapturing abdominal orientation data and delivering feedback to theuser. The device may include at least one of: one or more sensors whichdetermine the spatial orientation of the user's abdomen (e.g., relativeto the direction of Earth's gravity); a microcontroller that receivesand stores orientation data from the sensors and estimates the level ofclinical risk over various time scales based on the abdominalorientation, and a communication device and/or output which conveysperiodic updates and alerts to the user on their current risk level. Asdescribed below in relation to FIGS. 3A-7B, in certain implementations,in use the device 10 may attach to another part of the body such as thechest, neck, shoulder, hip, abdomen, or back during the daytimeactivities and then may be placed in the sleep position belt during restor sleep. Further, in certain embodiments, the device 10 and/or sensorsmay be placed in a variety of locations or a combination of locations onthe body, and the algorithm used to process sensor data could be mademore or less complex to account for different placements. Additionally,the device could be calibrated to account for different placements andtypes of motion. In some embodiments, the device may have a low profileso that it cannot be easily seen if place externally.

Advantageously, some embodiments of the systems, devices, and methodsmay be customized for different users. For example, different women withdifferent physical attributes or seventies of disease may benefit fromsystems, devices, and methods utilizing specialized programs. In someembodiments, the risk assessment and type of feedback provided by thedevice may be influenced by a number of factors. For example, somefactors that may be accounted for include the user's height, weight,age, age of gestation, blood pressure, diagnostic test results (genetictests, blood tests, urine tests, etc.), time since diagnosis, priornumber of device alerts, and/or the doctor's or user's preference on therestrictiveness of their daily activities. Accordingly, in someembodiments, one or more of these factors may be inputted to create acustomized algorithm for individual users. As an example, one user mighthave preexisting hypertension, be obese, be at 35 weeks of gestation,and have a positive diagnostic result for genetic predisposition topreeclampsia. Once these factors are input into the algorithm, thesystems, devices, or methods may calculate that the user is at higherrisk for preeclampsia and may then provide alerts to the user that areappropriate for higher risk users. In some embodiments, the device couldprovide feedback over an extended period of time, preventing developmentof progression of the syndrome. For example, a customized device orsystem may be more sensitive for higher risk users—alerting the usereven when the user has not spent a lot of time in positions that arehighly contributive to pressure on intra-abdominal organs. In anotherexample, some embodiments may determine that a young, healthy woman at20 weeks of gestation is at a lower risk for preeclampsia and may thenprovide alerts to the user that are appropriate for lower risk users.Accordingly, the user may receive no warnings even when the user hasspent a similar amount of time in similar positions as the higher riskuser. Optionally, some systems and devices may be configured to suggestthat the user check their blood pressure, take a proteinurea test, orcheck in with their doctor. Alternatively, the device could beconfigured so that the doctor could communicate directly with thepatient.

Embodiments of the system 1 may calculate the risk associated with aplurality physiological parameters and perform a calculation based onthe accumulated risk of each of the plurality parameters over a periodof time. There may be high risk and low risk parameters but since it isan accumulated calculation, there may be no parameters that are offlimits. For example, a pregnant woman's IVC may be temporarily occludedif she lies in the supine position. If she only spends a few seconds inthat position and then rolls over on her side, blood flow will resumeand she will be fine; however, if she continues to move back to thesupine position repeatedly, blood flow may be restricted to a variety ofabdominal organs and she could be at risk for either an acute organdysfunction, or a prolonged stress that leads to a chronic organdysfunction or failure. Accordingly, some embodiments of the system 1estimate the pressure, mechanical force, and/or impulse placed onintra-abdominal organs over short time periods (e.g., seconds, minutes,or the like) and/or over very long time periods (e.g., months) and mayalert or notify users and/or clinicians if users are experiencing toomuch cumulative pressure on their organs and tissues.

While some embodiments are generally discussed in terms of monitoringorientation and activity of pregnant women, many methods, devices, andsystems may be applied to any other area of the body where differentpositions have different levels of risk or benefit associated with them.For example, users with obesity related hypertension may benefit frompreventing abdominal fat from chronically or periodically compressingrenal nerves and abdominal veins which may increase hypertension. Inanother example, in a user with back pain, the algorithm assignsdifferent levels of risk or detriment to different positions. Thecumulative negative impact over time that the user experiences while invarious positions is added up and compared to allowable risk-time levelsto determine whether the user should be alerted to change position inorder to prevent back pain or muscle or nerve inflammation. Devices andmethods may be beneficial to users suffering from gastroesophahaelreflux or other digestive disorder that require they spend time incertain positions. Other diseases that may benefit from embodimentsdisclosed herein include Chorea, Parkinson's, and heart disease.

According to one embodiment, as shown in FIG. 1B, the system also has anexternal server or processor or processors 2 running risk score software3. The processor 2 can have a central processor unit (“CPU”) and mainmemory, an input/output interface for communicating with variousdatabases, files, programs, and networks (such as the Internet), and oneor more storage devices 4. The storage devices may be disk drive devicesor CD-ROM devices. The processor 2 may also have a monitor or otherscreen device and an input device, such as a keyboard, a mouse, or atouch sensitive screen and may be connected to a network 8.

According to one implementation, the processor 2 is in communicationwith at least one parameter database 5. In various implementations, theparameter database 5 is configured for the accumulation of informationrelating to each physiological parameter sensor from the user device 10.The parameter database 5 contains information relating to any particularuser physiological parameter described herein, such as abdominalorientation, blood oxygen level.

Further, a risk score threshold database 6 may also be in communicationwith the processor 2. According to one embodiment, the thresholddatabase 6 contains information regarding physiological parameterthresholds in the population that are more likely to lead tocomplications or fetal conditions during pregnancy. In variousimplementations, the threshold database 6 may also be in communicationwith the network so as to revise the thresholds periodically orcontinuously on the basis of data gathered from various other users, instudies and the like.

The databases 5, 6 can serve as the inputs to and information storagefor the system 1, which processes the information as described below andgenerates any one or more of notifications, reports, suggested actions,and/or instructions to a user or a third party.

The external processor 2 allows access to various network resources. Inone embodiment, the central processor 2 also has access, via the network8 or some other communication link, to external data sources that may beused to keep the information in the databases 5, 6 current.

It is understood that the processor 2 can be any computer known to thoseskilled in the art. In one embodiment, the central processor 2 includesa website hosted in at least one or more computer servers. It isunderstood that any system disclosed herein may have one or more suchserver and that each server may comprise a web server, a database serverand/or application server, any of which may run on a variety ofplatforms.

In one implementation, the central processor 2 includes softwareprograms or instructions to process requests and responses. Thesesoftware programs or instructions perform calculation, compilation, andstorage functions, transmit instructions, and generate reports. It isunderstood that any embodiment of the systems disclosed herein thatprovide for data collection, storage, tracking, and managing can becontrolled using software associated with the system. It is furtherunderstood that the software utilized in the various embodimentsdescribed herein may be a software application or applications that arecommercially sold and normally used by those skilled in the art or itmay be a specific application or applications coded in a standardprogramming language.

It is further understood that the software can be any known software foruse with the systems described herein to track, calculate, and managethe various parameters as described herein. For example, as described infurther detail herein, various embodiments of the systems describedherein could have any one or more of software for tracking orientation,sleep, blood pressure, blood oxygenation, or software allowing foroptimization of any one of these parameters.

In the system, generally, reactive zone data (such as, for example, timeand temperature data, etc.) entered into the system via a clientcomputer or processor 2 is received by the processor 2 or server andstored in any of the appropriate databases of the system.

Various implementations of the disclosed devices, systems and methodscollect data about the user to monitor fetal risk. As shown in FIG.6A-7B the system 1 is configured for measuring or estimatingphysiological parameters, which can then be processed in the mannerhereinafter described. In exemplary implementations of the system 1, oneor more devices 10 are provided, as described above in relation to FIG.1 . In these implementations, the devices 10 can have multiple sensors14, such as body orientation sensors. As discussed below, in relation toFIG. 2B, the sensor data may include force data and orientation data.The force data may be F_(x), F_(y), F_(z) force data. The orientationdata may be a recline (pitch) angle (θ) and a side tilt (roll) angle(φ). Orientation in 3 dimensions may be defined as below. As is furtherdiscussed in relation to FIGS. 3A-5L, in various implementations, thedevice 10 or devices can be securely attached to the user's body. Asshown in FIGS. 6A-7B, in certain implementations, the sensors 14 canalso be blood oxygen sensors 92, acoustic sensors 93 and/or anaccelerometers 95. In further embodiments, photo sensors, pressuresensors, and ultra wide band (“UWB”) sensors can be utilized as well.These sensors 14 are paired with various microcontrollers 12 and/orwireless interfaces 20 (as shown in FIG. 1 ) so as to allow for thecollection, processing and transmission of the sensed data. Thesedevices 10 can therefore be securely disposed on the user's body so asto effectively track a number of variables.

In some embodiments, the device measures or estimates physiologicalparamenters using a one or more sensors 14, which can be placed in anyof a number of soft tissue or skeletal locations. In one example, ablood oxygen sensor 92 might be placed intra- or perivaginally, or onthe legs, feet, or toes. In another example, an accelerometer and bloodoxygen sensor might be placed on the body above the waist and a secondblood oxygen sensor that is placed below the waist, allowing theprocessor to compare collected physiological data and assess the risklevel or possible cause of an irregularity.

In some embodiments this may be done with two or more sensors. Forexample, a sensor can be placed on soft tissue, such as on the bellybutton, and another sensor placed on the skeleton, such as on thesternum. In further embodiments, just one sensor may be used. The one ormore sensors 14 may include accelerometers, gyroscopes, magnetometers,infrared/temperature sensors, pressure sensors, microphones, oximeters,ultra wide band microwave sensors, radio waves, infrared sensors and/orcombinations thereof. In some embodiments, the one or more sensors 14may be 3-axis sensors or a plurality of single axis sensors. Forexample, in some embodiments, device 10 may feature a user-programmablegyroscope with a full-scale range of ±250, ±500, ±1000, and ±2000°/sec(dps). In some embodiments, device 10 may feature a user-programmableaccelerometer full-scale range of ±2 g, ±4 g, ±8 g, and ±16 g. In someembodiments, the device 10 may feature a magnetometer full-scale rangeof ±4800 pT. The certain implementations, the device 10 may furtherinclude analog-to-digital converters for digitizing the output from theone or more sensors 14 for data recording and analysis.

In some embodiments, different parts of the abdomen may be used fororientation calculations including belly button or estimated center ofgravity. In some embodiments, a number of impulse vectors on differentorgans and tissues may be estimated by the device.

In some embodiments, the device estimates of the center of gravity ofthe soft tissue in relationship to the musculoskeletal system. In someembodiments this may be done with 2 sensors (one on soft tissue (e.g.,belly button) and one on skeleton (e.g., sternum)). In furtherembodiments, just one sensor may be used which requires empirical ortheoretical data to determine where abdominal soft tissue would beexpected to be in relationship to interior vessels or othertissues/organs, depending on the age of gestation, height and weight,and number of fetuses etc.

In some embodiments, the device captures various activities likerunning, driving, etc. and assigns specific values to those which aredifferent for near term and long term analysis. For example, in someembodiments, the device alerts a woman to take a break after 10 minutesof jogging or 30 min of walking, but the algorithm views those shortduration as net positives over the period of days or weeks. In someembodiments, the global intra-abdominal pressure or regionalintra-abdominal pressures are estimated by the algorithm. In someembodiments, the algorithm views an activity as beneficial for abdominalhealth initially but later views it as detrimental to health after acertain threshold time. For example, in some embodiments, walking may beinitially viewed as beneficial, but reaches an inflection point at 30minutes, at which point, it may be viewed as detrimental.

In some embodiments, different parts of the abdomen may be used fororientation calculations including belly button or estimated center ofgravity. In some embodiments, a number of impulse vectors on differentorgans and tissues may be estimated by the device.

In some embodiments, the device estimates of the center of gravity ofthe soft tissue in relationship to the musculoskeletal system. In someembodiments this may be done with 2 sensors (one on soft tissue (e.g.,belly button) and one on skeleton (e.g., sternum)). In furtherembodiments, just one sensor may be used which requires empirical ortheoretical data to determine where abdominal soft tissue would beexpected to be in relationship to interior vessels or othertissues/organs, depending on the age of gestation, height and weight,and number of fetuses etc.

In some embodiments, the device captures various activities likerunning, driving, etc. and assigns specific values to those which aredifferent for near term and long term analysis. For example, in someembodiments, the device alerts a woman to take a break after 10 minutesof jogging or 30 min of walking, but the algorithm views those shortduration as net positives over the period of days or weeks. In someembodiments, the global intra-abdominal pressure or regionalintra-abdominal pressures are estimated by the algorithm. In someembodiments, the algorithm views an activity as beneficial for abdominalhealth initially but later views it as detrimental to health after acertain threshold time. For example, in some embodiments, walking may beinitially viewed as beneficial, but reaches an inflection point at 30minutes, at which point, it may be viewed as detrimental.

Various sensors 14 can be paired with one another depending on thephysiological parameters to be assessed. For example, as shown in FIG.6B, a blood oxygen sensor 92 could be paired with a microphone 93 and anaccelerometer 95. In one embodiment, this combined sensor device 10compares blood oxygen to body orientation using the processorhereinafter described to create a specialized risk profile for eachuser. In one embodiment, this risk profile is updated throughoutpregnancy as the uterus grows bigger and causes more blood flowrestriction. It is understood that various alternate implementations ofsensor configurations are contemplated herein.

In many embodiments, the one or more sensors 14 may provide force data(e.g., F_(x), F_(y), F_(z)) and/or orientation data (e.g., a reclineangle θ, a side tilt angle φ) to the microcontroller 12 for processing.In certain embodiments, the device 10 is configured to trackphysiological parameters. In certain embodiments, the device 10 isconfigured to record sensor 14 data through the night or other period ofday time which can be reviewed later to determine whether the user iscomplying with the intended positional therapy. Exemplary processingalgorithms are discussed further below.

As shown in the embodiment of FIG. 2C, in one embodiment, the PCB,battery, vibrational motor and other components of the wearable deviceare distributed so that they are next to each other rather thanoverlapping on top of one another. They are each coated with awaterproof polymer barrier and then arranged spaced slightly apart fromone another and connected by wires and ribbon cables. They are thenplaced between 2 layers of fabric in a specially designed shirt. Thisarrangement makes the electronics as low profile as possible and alsoallows more flexibility of the shirt to contour more normally to thebody during typical wear and movement. In one embodiment, the electroniccomponents themselves are also flexible, such as a flexible circuitboard or flexible battery. In one embodiment, the shirt and electronicsare a disposable item so that when the battery runs out, the entiresystem is thrown away.

As depicted in FIG. 2D and FIGS. 3A-7B, in certain embodiments, thedevice 10 can be configured to be mounted on the user in a fixedorientation via an external mounting device 28. In variousimplementations, this can be done by adhesive 29 (as shown in FIG. 2D),housed in a garment 30 (FIG. 3A-3I or 7A-B), or through use of anexternal mounting device or clip 74 (FIGS. 4A-5L) so that it maintainsthe same relative orientation to the user at all times. In variousimplementations, these external mounting devices 28 can have a varietyof sensors, as is shown in FIGS. 6A-6B.

In the embodiments of FIGS. 3A-I, the external mounting device 28 is agarment 30 for keeping the device 10 in a fixed orientation can have apouch or pocket 32. As shown in FIGS. 3A-F, in various embodiments thepocket 32 is configured such that the device 10 fits snugly into thepocket 32 and does not fall out during normal activity of the user 40.

In certain implementations, and as shown in FIGS. 3A-C, the garment 30has multiple pouches 32, 32A aligned vertically. In these embodiments,the user 40 can place the device 10 in any of the pouches 32, 32A asdesired for comfort and use. In certain implementations, the pocket 32may be oriented so that the device 10 is in the same orientationrelative to the user 40 each time the device is placed within the pocket32. In exemplary embodiments, the pocket 32 has a first end 34 and asecond end 36, and can be sewn onto the front of the shirt 30 just belowthe breasts and is very slightly smaller than the device 10 so that thedevice fits snuggly into the pocket 32 between the first end 34 andsecond end 36 without moving in relationship to the pocket 32. Incertain embodiments, the pocket 32 has an opening 38 on the first end 34of the pocket 32, which can be slightly smaller than the rest of thepocket.

As shown in FIG. 3A-C, the garment 30 is a shirt that fits snugly aroundthe chest just below the breasts of the wearer 40. In certainembodiments, the garment 30 might have a support band 42, which isplaced below the breasts and extends around the torso 44 so that thedevice and pocket are held more securely against the body of the user40, or an integrated strap 46 fitted around the chest just below thebreasts. In various implementations, the integrated strap 46 isadjustable and allows the user to tighten or loosen the strap to adjustcomfort as well as dictate how firmly the sensor device is held againstthe body of the user 40. In alternate embodiments, the garment may belooser fitting.

As shown in FIGS. 3C and 3F, in certain embodiments, the device 10 isplaced into a pocket 32, the device 10 prompts the user to recalibrate.In one embodiment, the interior of the pocket 32 has a conductive region50 disposed such that placement of the device 10 in the pocket 32 issensed by at least one electrode 52, 54 disposed on the device 10 andconfigured to come into electrical communication with the conductiveregion 50 so as to close a circuit, as would be appreciated by a skilledartisan. In an alternate embodiment, the device 10 can have a projection56 that is triggered when the device 10 is placed in the pocket 32. Invarious embodiments, the projection 56 can be a lever, button, magnet orother sensor that alerts the device to require a calibration.

As shown in FIG. 3G, in certain implementations, the garment 30 furthercomprises a belt 58 with a support 59. The support 59 or supports can befoam pads or projections integrated into the belt 58 so as to preventthe user from assuming a supine position while seated. These supports 59thereby discourage certain maternal sleep or daytime positions for thepurpose of preventing complications of pregnancy described elsewhereherein.

As shown in FIGS. 3H-1 , in certain implementations, the garment 30 hasat least one magnet 60 which are detected by a magnetometer 62 (FIG. 3H)or magnetometers 62A, 62B (FIG. 3I) disposed within the sensor device10. In one embodiment, the device 10 turns on or becomes functional whenthe magnetometers 62 sense the magnets 60 and know the device has beenplaced on the user. In some embodiments, the polarity of the magnets 60,60A is such that the device 10 can only be activated when it is placedin the proper orientation on the body. In one embodiment, if themagnetometers 62 detect magnets in the incorrect position, the device 10will issue an alert to notify the user the device is not positionedproperly.

Data conveying physiological parameters could be used in many otherways, including, but not limited to, determining whether the device wasbeing utilized, activating a power-save mode, determining whichphysiological profiles indicate high risk groups, suggesting tests andtreatments, calculating percent compliance, and recognizing high riskpatterns as a way of engaging in preventative treatment. In someembodiments, the data could be communicated in a variety of ways,including, but not limited to, wireless intra-sensor communication,wireless connectivity to a mobile device, or connection to the primarydevice via wires.

FIGS. 4A-G depict further implementations of a wearable device 70 and/orclip 74. It is understood that the wearable device 70 of FIGS. 4A-4Ghaving magnets can be any of the devices disclosed elsewhere herein inreference to number 10.

As shown in FIG. 4C, in one embodiment, the device is designed to bepositioned on the torso with an arrow 78 on the device face 80 pointingupwards. As shown in FIG. 4A, in a further embodiment, the mating piece74 and device 70 have protrusions 82 or indentions that fit together.This geometric interlocking prevents the parts from rotating in relationto each other. Since the magnets 72A, 72B, 76A, 76B urge the device 70and mating piece 74 tightly together, the projections 82 arecorrespondingly pulled into the indentions and thus when trying torotate the parts in relation to each other, they encounter mechanicalinterference.

In one embodiment, as shown in FIG. 4D, the mating piece 74 issymmetrical in at least two planes so that the mating piece 74 canmagnetically attach to the device in a wide variety of orientations.This eliminates the need to carefully align the mating piece under theclothes with the device above the clothes. No matter how the matingpiece is oriented, it will immediately clip in place correctly.

As shown in FIGS. 4E-F, various implementations of the system 1 have awearable device 70, in which at least one magnet 72A, 72B is attached orembedded, and a mating piece 74 also having at least one magnet 76A,76B, wherein the magnets are arranged so that the two parts onlymagnetically connect in predetermined preferred orientations. In variousembodiments, once engaged, these magnets 72, 74 prevent the wearabledevice 70 from easily rotating. In exemplary embodiments, the device 70and mating piece 74 each have two magnets 72A, 72B, respectively, suchthat they can only attach in one orientation due to the polarity of thevarious magnets. This prevention of rotation ensures that the user isalways wearing the device in the proper orientation so her orientationrisk can be calculated accurately.

In one embodiment, the centers 71A, 71B, 73A, 73B of the magnets areabout 0.5 inches apart. This separation 71A, 71B prevents the device 70and mating piece 74 from easily rotating in relationship to one another.

In one embodiment, the device 70 and mating piece 74 are configured tobe mounted on opposite sides of a cloth garment, such as a shirt. Thisconfiguration allows the sensors—such as the accelerometer andmicrophone—to be disposed inside or outside of the garment, as desired.

In one embodiment, as shown in FIG. 4G, one or more magnets 72A, 72B areattracted to ferrous metal mounts 75A, 75B that have a similar shape asthe magnets 72A, 72B. In an exemplary embodiment, the sensor device 10has magnets 72A, 72B which are 1 cm×1 cm×1 mm disposed 1 cm apart. Themating clip 75 has ferrous metal mounts 75A, 75B which are also 1 cm×1cm×1 mm and are also 1 cm apart from each other. When the clip 75 andthe device 10 come in close proximity to each other, the magnets willhave a natural tendency to attract to the metal squares such that themagnets 72A, 72B and mounts 75A, 75B will only attract to each other inone of 2 possible orientations, “right side up” and/or “upside down.”Any other orientation will self-correct into one of the preferredorientations.

As best shown in FIGS. 5A-J, certain embodiments of the system 1 furthercomprise a clasping device, or clip 84. In these implementations, theclip 84 has a clasping region 85 and a coupling region 86. In variousembodiments, and as shown in FIGS. 5A-B, the clasping region 85 has afirst arm 85A and a second arm 85B. Each of the arms 85A, 85B isdisposed around a first hinge 88, and can have clasping portions 87A,87B and/or magnets 89A, 89B configured to create a physical and/ormagnetic connection, for example around an item of clothing, such as abra.

As shown in FIG. 5C-L, in these implementations, the coupling region 86is configured to accept the wearable device 90. In various embodiments,the hinge 88 not only allows the clasping region 85 to open and close,but also allows the clip 84 to freely move with respect to the claspingregion 85. This is important because a pregnant user's abdomen willincrease in size throughout pregnancy, so if the clasping region arms85A, 85B are rigid in relation to the rest of the clip 84 and device 90the hinge these components will uncomfortably dig into the abdomen orsternum. Similarly, if the user bends forward at the waist, the cliphinge needs to rotate so the clip does not dig into the skin.

In one embodiment, the magnets 89A, 89B are disposed below the claspingportions 87A, 87B so that they can come in direct contact with eachother rather than in just “close” contact through the bra material. Thisenables the clasping region 85 to have the strongest possible connectionwith the minimal amount of magnet material since magnetic force falls asthe inverse cube of the distance. By having the magnets 89A, 89B contacteach other they have the strongest possible attractive force to eachother. This arrangement also allows the projections to have a very thinprofile since the magnets and ridges and projection material don't allneed to be layered on top of each other.

In one embodiment, the magnets 89A, 89B are not visible, but are coveredby a layer of fabric or plastic or other material on the clasp (notshown). However, the arrangement of the magnets below the claspingportions 87A, 87B allows for a strong magnetic connection as themagnetic force does not need to span the distance created by the pinchedbra fabric or the clasping portions 87A, 87B.

In one embodiment, the outside surface of the clasping region 85 betweenthe bra and the skin is flat so that it is not uncomfortable against theskin. In another embodiment, the clasping region 85 on the outside ofthe bra is curved away from the bra at the top so that a user's fingerscan easily pry the clasp open.

In one embodiment, the clip 84 does not have magnets but instead uses amechanical latch. In one embodiment, the clip 84 does not have magnetsbut instead uses clasping portions 87A, 87B that are flexible enoughthat they can be pried apart and slipped over the bra material but rigidenough so that they create a firm grip on the bra material.

In one embodiment, the clasping portions 87A, 87B are sufficiently longso that when the clasping portions 87A, 87B clasp the bra material, theclip 84 and device 90 do not significantly swing left or right when theuser lays down on her side. This is important so that the device 90 isable to identify whether the user is on her side or is on her back orupright.

In various embodiments, the hinge 88 is a standard hinge. In variousembodiments, the hinge 88 is a living hinge made of flexible materialthat allows the clasp to open. In various embodiments, the hinge 88 is alocking hinge that is freely moveable until it is locked in place. Forexample, the device could be attached to clothing in a variety ofconfigurations including, but not limited, a to a hinge, anautomatically locking hinge, a user-activated locking hinge, or Velcro.

In one embodiment, the clip 84 attaches to the bottom of the bramaterial between the cups. This ensures that the device 90 hangsstraight down from the center of the sternum so that is aligned properlyon the body.

In certain implementations, the system utilizes a material worn on theabdomen or chest that reflects UWB waves and a device that transmits UWBwaves towards the body and receives the reflected signal. In theseimplementations, a device can be utilized by running an algorithm thatidentifies breathing characteristics based on the movement of the chest.In certain embodiments, the system uses material having a distinctpattern which can be recognized by an algorithm, as would be understood.For example, in one embodiment, the material has a repeating geometricpattern of holes. In another embodiment, the material has anon-repeating unique pattern that can be used to determine chirality ofthe reflective material. In this case, the algorithm can tell whetherthe UWB waves are going through the body and then reflecting off of theback surface of the material or are bouncing off the front of thematerial before penetrating the body. In some embodiments, thereflective material is one or more of the following materials:aluminized mylar, thin strips of metal, reflective polymers, or metalmesh.

In further embodiments, the device captures various physiologicalparameters and provides feedback based on the intensity and time span ofthe measurement collected. For example, in some embodiments, the devicealerts a woman to take a break after 10 minutes of jogging, but thealgorithm views those short duration as net positives over the period ofdays or weeks, however the data but reaches an inflection point after 10minutes, at which point the time span could be considered detrimental.In one embodiment, the blood oxygen sensor 92 identifies variousrespiration characteristics such as normal breathing, shallow breathing,snoring, apnea or no breathing and assigns them different risks. Whenthe moving average risk crosses a predetermined threshold an alert isissued. In additional embodiments, and as shown in FIG. 6B, the device10 also uses a microphone 93 and/or accelerometers 95 to measurebreathing characteristics.

In another example, as shown in FIG. 7A, the device 10 could have ablood oxygen sensor 92 integrated into a pair of “smart underpants” 94.In certain embodiments, also shown in FIG. 7A, the “smart underpants”could have a pocket 96 configured to house the blood oxygen sensor 92 ordevice 10. Alternatively, as shown in FIG. 7B, the underpants 94 couldhave sensors 10A, 10B, 10C in different locations to get a more accurateindication of orientation and blood oxygen level. For example, a systemof sensors attaches to an existing pair of underpants or a speciallydesigned pair of underpants. The device can be removed so that thedevice can be easily cleaned and used with a clean pair of underwear.

Returning to FIG. 2B, an exemplary coordinate system for the user 40 andthe device 10. In FIG. 2B, forces along the user's 40 left to right sidemay be represented by measured forces along the x-axis (left side toright side of the device). Forces along the user's vertical axis (e.g.,feet to head) may be represented by measured forces along the y-axis(bottom of the device to the top of the device). Forces from the user'sback to front may be represented by measured forces along the z-axis(front surface to back surface of the device).

Also illustrated, rotation values around the x-axis (called the reclineangle and represented by θ) may range from lying face down tositting/standing straight up to lying face up. Further, rotation valuesaround the z-axis (called the side tilt angle and represented by φ) mayrange from lying on the left side to sitting/standing straight up, tolying on the right side. It should be understood that the illustratedcoordinate system is exemplary and not limiting.

In certain implementations, the device must be calibrated. In variousimplementations, the device 10 can be calibrated to account fordifferent variations on physiological parameters. In one example, thedevice may be placed in an imperfect orientation on the body and thencalibrated appropriately. The user uses a magnetic clip to attach thedevice to her shirt, so that the device is reclined by 20 degrees andtilted to the left 30 degrees. A calibration sequence is initiated whenthe user is standing still in the upright position and presses acalibration start button. Optionally, the calibration procedure mayprovide a brief pause (e.g., 1-5 seconds) between when the user pressesa calibration start button and when the calibration calculations beginin order to allow the user to get into a preferred position. In anotherexample, the system has a snoring calibration function where the userwears the device and makes snoring sounds in order to determine thecorrect default values for the snoring algorithm. The device could tellthe user when to make sounds and could use the processor algorithmadjust the threshold value according to the calibration.

The calibration may be performed by taking the average of each of the x,y, and z values over a time or measurement period (e.g., last 10measurements or last 1-10 seconds) and checking to see if values of eachof the x, y, and z variables are relatively unchanged. If unchanged,this indicates that the user is standing very still, and thus, thecalibration constants may be recorded. For example, if measurements arereceived at 0.1 second increments, at 0.1 seconds, a first data pointmay be recorded (x₁,y₁, z₁), at 0.2 seconds, a second data point may berecorded (x₂, y₂, z₂) and so on over the calibration time period (1-10seconds for example). The new data point generated every 0.1 seconds maybe put into a new variable (e.g., x_(new), y_(new), z_(new)). At every0.1 second increment, the calibration algorithm may check if thesubsequently received values are within a desired percentage (1-5%) ofthe initial reading (e.g., x₁, y₁, z₁). For example, the algorithm maycheck to see if the new variables are within 2% of the initial reading:0.98*x _(new) ≤x ₁≤1.02*x _(new)0.98*y _(new) ≤y ₁≤1.02*y _(new)0.98*z _(new) ≤z ₁≤1.02*z _(new)

If the equation is satisfied, it may continue to the next measurement.If at any point, any of the values are not within the desiredpercentage, the calibration sequence may restart with a new initialreading (e.g., x₁, y₁, z₁). When the calibration time period (1-10seconds for example) passes without any of the x, y, or z valuesfluctuating more than the desired percentage (e.g., 2%), the calibrationconstants may be recorded and an orientation calibration may beperformed where:

${{cal}.{theta}} = {\arctan( \frac{z_{1}}{y_{1}} )}$${{cal}.{phi}} = {\arctan( \frac{x_{1}}{y_{1}} )}$

Variables cal.theta and cal.phi may be subtracted from the raw theta andraw phi values. As would be apparent to one of skill in the art, variousalternative algorithms can be used to establish the theta and phicalculations.

In one embodiment, the calibration algorithm is given by:cal.phi=arctan(y1/sqrt(z1{circumflex over ( )}2+x1{circumflex over( )}2)*180/pi)cal.theta=arctan((−x1/((sign of z1)*(sqrt(z1{circumflex over( )}2+0.01*y1{circumflex over ( )}2))))*180/pi)

In these embodiments, whenever the device acquires sensor data, it willsubtract the “cal.theta” and “cal.phi” variables from the theta and phivalues calculated from the raw data coming into the algorithm from thesensors. As such:

-   -   Raw data is given by:        sensor.phi=arctan(y/sqrt(z{circumflex over ( )}2+x{circumflex        over ( )}2)*180/pi)        sensor.theta=arctan((−x/((sign of z)*(sqrt(z{circumflex over        ( )}2+0.01*y{circumflex over ( )}2))))*180/pi)    -   Calibrated data is given by:        real.phi=sensor.phi−cal.phi        real.theta=sensor.theta−cal.theta

In some embodiments, the following alternate xyz plane is defined as:

-   -   y=g's of force from y-sensor (axis of person's left hip to right        hip)    -   x=g's of force from x-sensor (axis of feet to head)    -   z=g's of force from z-sensor (axis of back to front)

In some embodiments, the phi and theta equations are modified in orderto stabilize the functions where:r=sqrt(x{circumflex over ( )}2+y{circumflex over ( )}2+z{circumflex over( )}2)phi(ϕ)=arctan(y/sqrt(x{circumflex over ( )}2+z{circumflex over( )}2))*180°/πtheta(θ)=arctan(−x*(sign of z)/sqrt(z{circumflex over( )}2+u*y{circumflex over ( )}2))*180°/πwhere u is a constant with a preferred range between 0.001 and 0.3, andwhere “sign of z” simply inserts a negative 1 when z is negative and apositive 1 when z is positive.

In certain embodiments, the device can also be calibrated to recognizecertain positions or to stop reading any combination of x, y, or z axisoutputs. In one example, the microphone and processor could beconfigured detect only the amplitude of sound and recognize snoring byidentifying short regular amplitude bursts over a set period of time, orit could be configured to analyze both the amplitude and frequency ofthe sound to determine if the sound is snoring or some other ambientnoise.

An example of this approach is the following: every body orientation hasa set amount of time in which the user is allowed to be before an alertis issued. In one embodiment, every single unique combination of sidetilt and recline angles has a different total amount of time associatedwith it. In one embodiment, a recline angle of −90 degrees with 0degrees side tilt (flat on back) has the very lowest amount of timeallowed (2 minutes). A combination of recline angle −73 degrees and sidetilt 7 degrees has a maximum time of 4 minutes and 22 seconds. Acombination of −35 recline and −82 side tilt has no maximum time value(infinity) because it is considered completely safe. Every time the usermoves orientations, the algorithm instantly calculates how much time theuser has left in that position. The remaining time is transferred andprorated to the new position. So, if the user has been flat on her backfor 1 of the maximum 2 minutes allowed by the algorithm and then shiftsto recline −75 and side tilt 7, she will have 50% of the time left forthe new position (2 min and 11 seconds).

In one embodiment, the system is a health monitoring system with one ormore sensors for generating sensor data, configured to identify healthrisk values associated with the sensor data; assign the risk value to acorresponding time value; adjust the time values continuously based onnew risk values; and output a warning when the risk value persists forthe direction of the time value.

In various embodiments, the following variables and logic are used inthe algorithm to establish the risk of time spent in various “zones”.For purposes of these implementations, there are different zones thatthe user can be in for different amounts of time. In one suchimplementation, each zone has a threshold value (“Zone1Thresh”,“Zone2Thresh” etc . . . ) associated with it as well as its own healthtime (“Zone1Time”, “Zone2Time”, etc . . . ). The health threshold valuesare in ascending order starting from zone 1.

In one exemplary embodiment, the system defines 3 zones that have thefollowing values:

-   -   Zone 1: 0.2 health score threshold for 20 sec.    -   Zone 2: 0.4 health score threshold for 30 sec.    -   Zone 3: 0.6 health score threshold for 60 sec.

The device vibrates when a user is below a threshold level for theamount of maximum time for that zone's health threshold. In variousimplementations, if a user is not in zones 1, 2, or 3, an alert will notbe generated. But, if the user goes into zone 1 and stays there for 20seconds, an alert is generated. If the user goes into zone 2 and staysthere for 30 seconds, an alert is generated. And, if a user goes intozone 3 and stays there for 60 seconds an alert is generated.

In these implementations, if a user goes into a zone for less than thealert threshold time and then transfers into a new zone, the time iscarried over into the new zone in a prorated manner. For example, takethe case where a user is only allowed to spend 60 sec in zone 3 or 30sec in zone 2 before an alert is triggered, where zone 2 is consideredless healthy than zone 3. If the user has been in zone 3 or 40 secondsand then transfers into zone 2, the algorithm will multiply 40 sec times30/60 (ratio of max time in zone 2 over zone 3) for a new value of 20sec. The user will now have only 10 seconds (30 sec max minus 20 secprorated carryover from previous zone) left in zone 2 before the alertis issued.

In one example, a user has been at a health score of 0.7 for an hour andthey then change position so their new score is 0.5. They are in thatposition (and in zone 3) for 30 seconds and then transfer into a newposition and have a health score of 0.3 (zone 2). The 30 seconds theyspent in zone 3 is now carried over to zone 2 but the time is proratedto account for the difference in maximum times between zones.

Similarly, in a new case where the user has spent 20 sec in zone 2 andthey transfer into zone 3, the algorithm behaves as though the user hasbeen in range 3 for 40 seconds (20 sec times 60/30) and will have 20seconds (60 sec max-40 sec carryover) until the alert is issued.

If the user goes to a position that has a health score higher than 0.6so they are out of the alert zones all together, the clock resets backto 0.

In a further embodiment, the number of consecutive vibrational alertsare programmed uniquely for each zone. In one embodiment, zone 1=5consecutive vibrations, zone 2=3 consecutive vibrations, and zone 3=1vibration.

In another embodiment, the risk and time values are based on continuousequations so there are an infinite number of risk and time levels. Inone embodiment, different risk equations are used to cover differentquadrants of space.

In another embodiment, more than 1 real time risk variable is used inthe risk calculation equation. In one embodiment, the overall risk is adirect function of both body orientation and snoring so that the alertis more likely to go off when the user has high risk values for each ofbody orientation and snoring.

In another example implementation, a wearable device 10 having amicrophone and a processor creates a time series of estimated fetalactivity scores where the fetal health is related to both the frequencyof and intensity of fetal movements as detected by the microphone. Anaverage fetal health score is calculated as an average of all the fetalactivity over a period of 4 hours. If the average fetal health scoredrops below a threshold level at any point, an alert is generated. Inanother embodiment, the processor counts all the fetal movements over acertain threshold size in a given period of time. If the total number ofmovements in a 4 hours period is less than the threshold number (forexample 20), an alert is generated.

In one example, the system 1 could have a device 10 for assessing andcorrecting sleep position which can be worn in any orientation on thechest or neck or abdomen during sleep as long as it is relativelyparallel to the bed mattress surface. During “night mode” an algorithmonly uses measures the z-value (direction perpendicular to themattress). When z=−1, the user is facing upwards. When z=0, it isassumed the user in on her side since the user is not likely to bevertical when asleep. By using only the z direction, the user no longerneeds to put the device on the body in a certain orientation and if thedevice rotates during sleep the algorithm will still accurately assesssleep position.

In various implementations, the device and system can provide alerts tothe user. The feedback device 22 may have one or more displays, lightindicators, speaker(s), and/or vibration motor(s) for outputting signalsto a device user. In certain embodiments, the device 10 is configured toissue vibrational alerts. The feedback device 22 may also provide anaudio output. For example, the feedback device 22 may provide beepingwarnings or vocal feedback/suggestions to the user. The feedback device22 may also provide a haptic feedback with a vibration motor. Forexample, the device 10 could use a blood oxygen sensor 92 to measure aphysiological parameter, and could then issue an alert described abovewhen blood oxygen falls below a certain level. In some embodiments, whenthe cumulative orientation risk value rises above the medium riskthreshold, the device may vibrate, beep, or flash once every two minutesuntil the cumulative orientation risk value improves and drops below thethreshold. If the cumulative orientation risk value rises above the highrisk threshold, the device may vibrate, beep, or flash twice everythirty seconds until the cumulative orientation risk score drops belowthe high risk threshold. The device may provide no feedback when thecumulative orientation risk score is below the medium and highthresholds. Optionally, the device may provide a visual feedback whenthe cumulative orientation risk score is below the medium and highthresholds (e.g., a green indicator or the like). The described feedbackand thresholds are exemplary. It should be understood that the feedbackalerts may have any number of configurations and may be customized by aclinician or a user.

For example, a display may display risk scores, orientations, activitylevels, etc. to a user. Optionally, the device could provide specificverbal advice regarding body orientation or medication needs, forexample, “don't lean back so far.” Additionally or alternatively, lightindicators may provide feedback. For example, light indicators may be arow of five lights that progressively light up to provide a warning to auser. Optionally, the light indicators 24 may provide various coloroutputs for different degrees of warning (e.g., green, yellow, red,etc.), or may signal power status, battery status or connectivitystatus. A variety of vibration intensities or audio volumes might beused depending on user position.

In some embodiments, the alerts could be configured to give morespecific feedback. For example, the device 10 has an arterial oxygensaturation (“SpO₂”) sensor that can be used when needed. The systemcould remind the user to activate SpO₂ functionality only on certainnights depending upon sensor results over a specific time span. At onetime, a user may have multiple previous sleep sessions where their SpO₂levels are very good and they also snore very infrequently; thereforethe system would not recommend that they use the SpO₂ attachment on agiven night. Conversely, at another time, if the system sensed low SpO₂levels and significant snoring many nights in a row, the system mayrecommend that the user use the SpO₂ sensor on a given night.

In some embodiments, the device may include a training system alertswhich teach the user which orientations or activities are consideredrisky. For example, in training mode, the device buzzes once when theuser enters a position of risk level 0.2 to 0.39, buzzes twice for risklevel 0.4 to 0.59, buzzes 3 times for risk level 0.6 to 0.79, and buzzescontinuously for risk level 0.8 to 1. In alternate embodiments, thealgorithm can be adjusted to alternate ranges with slightly differentmaximum amounts of time allowed based on their level of risk. In oneembodiment, all body orientations fit into 1 of 100 different categoriesfrom Range 100 (healthiest) to Range 1 (least healthy). A minimum healththreshold can be set so that all Ranges above a certain level of healthnever cause the algorithm to issue an alert. In this case, all Rangesover 45 are considered healthy for a specific user. The user can spendvarying amounts of time in Ranges 1 through 45 before an alert isissued. In this case, the user can spend 10 sec plus (“the range #”times 2). So, in Range 45, the user can spend 100 seconds (10+45×2)before an alert is issued. In Range 20, a user can spend 50 sec(10+20×2). In range 1, a user can spend 12 seconds (10+1×2). And, ineach case, time spent in other ranges affects the amount of time theuser can spend in the new range. This time carryover affect can happencontinuously until an alert is issued or the user enters a healthorientation (Range 45 or higher).

In some embodiments, the processor may use algorithms to calculateforce, pressure, or other measurements, which would show how the body isbeing affected by different physiological parameters. In one embodiment,the algorithm could be constructed using imaging techniques andempirical data. In other embodiments, the algorithm could be adaptedbased on physiological data from other users. In alternate embodiments,the processor could use an algorithm to determine whether a givenphysiological parameter value is caused by a global or localizedstimuli.

For example, the system could detect snoring and use an algorithm toadjust the data sensor measurements. In this example, the deviceacquires microphone sound amplitude or voltage data at a rate called“MicSampleRate,” typically occurring between 0.5 Hz and 10 Hz. Analgorithm is then employed by the system 1 to detect sounds that exceedcertain defined noise and frequency thresholds. For example, the systemcould identify user snoring by uses a “SnoreStartConsec” parameter. Forexample, if the “SnoreStartConsec” parameter is set at four, the devicewill begin to collect data when the microphone and system detect four ormore consecutive amplitudes above “HeavySnoreStartAmpThresh,” which byway of example can be set at about 50 db and/or“LightSnoreStartAmpThresh” which by way of example can be set at about30 db. Likewise, the system could also detect when a period of snoringhas ended by employing another algorithm defining low volumes andfrequency. For example, when there are two or more sounds consecutivedetected amplitudes that qualify as “SnoreEndConsec” (default value 2)consecutive (or more) amplitudes below “HeavySnoreEndAmpThresh” (defaultvalue of 20 db) or “LightSnoreEndAmpThresh” (default value of 10 db).Between these two functions, the device could collect data regarding theuser's snoring profile.

Variable “SnoringState” is generated at the same rate as“MicSampleRate”. It may have 4 different values: heavy snoring, lightsnoring, not snoring, unknown.

In the previous embodiment, the default values of the variables maychange based on specific user characteristics such as whether they havea partner who snores or how close the device is to their mouth.

In some embodiments, the processor would calculate the risk associatedwith a certain physiological parameter. For example, the system 1 mightbe used for monitoring breathing during sleep. Sensors would collect thedesired physiological parameter and use an algorithm that identifieshealth risk values associated with the sensor data to produce a timeseries of identified risk values. The cumulative risk value could bedetermined and updated by calculating a moving average for a subset ofthe time series of identified risk values associated with the sensordata. Then, the cumulative risk could be compared a threshold value, andthe device could output a warning when the cumulative risk value crossesthe first threshold. In a further embodiment, the algorithm assigns arisk score based on the breathing rate, where consistent regularlyspaced breaths are given the highest score and long intermittentstoppages in breathing are given the lowest score. An exemplary equationthat would be part of this algorithm is provided by:

-   -   Healthiest score=1,    -   Riskiest score=0;    -   Score over last 10 minutes=1/(2*# of breathing stoppages greater        than 10 seconds){circumflex over ( )}0.5.

Similarly, in another example, the length of the breathing stoppageimpacts the risk score generated: Score over last 10 minutes=1/(3*# ofbreathing stoppages greater than 15 seconds+2*# of breathing stoppagesgreater than 10 but less than 15 seconds){circumflex over ( )}0.5. Inthese implementations, the processor could use an algorithm to calculatea moving average as described above. In one embodiment, the breathingalgorithm is as follows:

-   -   a. Acquire accelerometer data at a rate called        “BreathSampleRate” (between 0.5 Hz and 30 Hz; default 8 Hz).    -   b. “BreathSignal” equals accelerometer value        “Pctx”*“accelx”+“Pctz”*“accelz”.    -   c. “accelx” equals the accelerometer value in the x direction,        “accel y” is the y direction accelerometer value    -   d. “Pctx” and “Pctz” have values from −100% to 100% (−1 to 1)        where the absolute values of “Pctx” and “Pctz” must equal to one        but one or both can be negative (eg in one example BreathSignal        is 0.7x−0.3z).    -   e. Variable “BreathState” is generated at the same rate as        “BreathSampleRate”. It may have 4 different values: inhale,        exhale, none, unknown.    -   f. “BreathSignal” is logged continuously for the last        “BreathTestDuration” seconds (default 5 sec). The most recent        “Breath #” (default 3) “BreathSignal” recordings are averaged to        create “RecentAveBreathSignal” and the next “Breath #” (eg 3)        most recent “BreathSignal” values are averaged into        “PastAveBreathSignal”.        -   i. For example, if “Breath #”=3, then            “RecentAveBreathSignal”=the average of the 3 most recent            “BreathSignal” values and “PastAveBreathSignal”=the average            of the 4th, 5th, and 6th most recent.    -   g. If the absolute difference between “RecentAveBreathSignal”        and “PastAveBreathSignal” is greater than the variable        “BreathNoiseMargin” (default value 0.1 g) then “unknown” is        recorded for “BreathState”.    -   h. If “RecentAveBreathSignal” is greater than        “PastAveBreathSignal” by “DynamicBreathMargin” (default value        0.005 g) then “BreathState” is given a value of “inhale”. If        “RecentAveBreathSignal” is less than “PastAveBreathSignal” by        “DynamicBreathMargin” then “BreathingState” is recorded as        “Exhale”. If they are within the “DynamicBreathMargin” range (eg        between −0.005 and 0.005), then “none” is recorded.    -   i. The algorithm continuously reviews the last        “BreathTestDuration” (eg 5 sec) seconds to determine if a full        breath has occurred. A binary variable “BreathPeak” is recorded        whenever at least “% breaths” (default 50%) of the first half of        the time segment (2.5 sec in this case) is inhale and at least        “% breaths” (50%) of the 2^(nd) half of the segment (2.5 sec) is        exhale.    -   j. “AveBreathRate” is recorded as the average time between all        individual “BreathPeak” values which do not have an “Unknown”        event between them.    -   k. “NoBreathCount” is increased by 1 every time no BreathPeaks        are recorded for “NoBreathTime” (default 15 sec). Each time this        occurs a variable “NoBreathEvent” is recorded as the time        between breaths. Note, this calculation is performed when a new        breath is detected so that the time since the last breath can be        recorded. Otherwise, it will record a time of 30 sec for each        one.    -   l. A full intensity vibrational alert (5 bursts) may be issued        (optional) when “#NoBreaths” (default 3) occur within a window        of “NoBreathTimeWindow” (default 10 min).

In one embodiment, the algorithm has the ability to detect breathingduring a subset of the night in order to limit battery consumption. Forexample, variables “TimeBreathDetectOn” (default value 2 minutes) and“TimeBreathDetectOff” (default value 28 minutes) would test breathingfor a 2 min segment every half hour. Setting “TimeBreathDetectOff” to 0makes breathing detection continuous through the entire night.

FIG. 9 illustrates an exemplary method 200 for monitor orientation risks110, for example in preventing preeclampsia. At step 202, orientationsensor data is received. Based on the received orientation sensor data,a determination of an orientation risk value can be made 204. Steps 202and 204 may be repeated for a continuous stream of orientation sensordata to generate a time series of orientation risk values 206. From thetime series of orientation risk values, a cumulative orientation riskvalue may be calculated 208. The cumulative orientation risk value maythen be compared to an orientation risk threshold 210. Feedback may thenbe outputted to the user 212 based on the comparison of the cumulativeorientation risk value to the orientation risk threshold. Optionally,the method 200 may further include receiving user data 214. Anorientation risk threshold may be calculated or adjusted in response tothe received user data 216 to provide a customized orientation riskthreshold. This customized orientation risk threshold may be used in thecomparison 212.

The orientation data may be a recline angle (θ) and a side tilt angle(φ). Each combination of θ and φ may correspond to an “Instant PositionRisk Score.” For example, this score may range from 0-1 (or scalesthereof) where 1 may be indicative of the most dangerous orientation.Continuing with the exemplary scale, in some embodiments, oriented facedown (while not lying on the stomach) may be valued at 0-0.05,preferably about 0.03; leaning forward at 45 degrees may be valued at0.05-0.10, preferably about 0.08; standing straight up may be valued at0.08-0.12, preferably about 0.10; leaning back at 45 degrees may bevalued at 0.35-0.45, preferably about 0.4; and lying on back may bevalued at 1.0.

FIG. 10A illustrates exemplary orientation risk values for combinationsof phi and theta. These orientation risk values may be stored as a lookup table and accessed by the processor to associate orientation riskvalues to received orientation data. Alternatively, the processor mayimplement orientation risk value equations to calculate the orientationrisk values.

For example, in the illustrated table of orientation risk values, sixconstants are provided: risk value for laying on the left side (“leftside risk” i.e., when phi is equal to −90 degrees), risk value forlaying on the right side (“right side risk” i.e., when phi is equal to90), risk value for a headstand (“headstand risk” i.e., when theta is180 or −180 and phi is 0), risk value for flat on stomach (“on stomachrisk” i.e., when theta is −90 and phi is 0), the risk values forreclining by more than −90 degrees when not tilted sideways (“recline bymore than −90 risk” i.e. when theta is between −90 and −180 and phi is0), and risk value for standing upright (“standing risk” i.e., whentheta is 0 and phi is 0). These constants may be defined by a clinicianand may be adjusted for fine tuning the orientation risk values in thematrix (e.g., to provide customized risk values specific for the user).The remaining risk values may be determined based on the six definedconstants.

In the exemplary matrix the six constants may be defined as follows:

-   -   risk_(left side)=0    -   risk_(right side)=0.3    -   risk_(headstand)=1.0    -   risk_(stomach)=0.01    -   risk_(−90<recline<−180)=1.0    -   risk_(stanading)=0.1

These constant values are exemplary for monitoring preeclampsia and maybe adjusted.

In the illustrated table, when the user is reclined backward by between−90 and −180 degrees (−90>theta>−180) and tilted to the left (phi isbetween 0 and −90), the risk value may be calculated by:

${risk} = {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk_{{left}\mspace{14mu}{side}}}} )*risk_{{{- 9}0} < {recline} < {{- 1}80}}*\sqrt{1 + {\sin( \frac{\pi\varphi}{180} )}}}}$

When the user is reclined backward between 0 and −90 degrees or −90degrees (i.e., 0<theta≤−90), and tilted to the left (phi is between 0and −90), the risk value may be calculated by:

${risk} = {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk_{{left}\mspace{14mu}{side}}}} )*( {{risk}_{standing} + {( {1 - {risk}_{standing}} )*( {1 - {\cos( \frac{\pi\;\theta}{180} )}} )}} )*\sqrt{1 + {\sin( \frac{\pi\;\varphi}{180} )}}}}$

Further, when the user is recline backward or flat on his/her back(i.e., 0<theta≤−90 and phi is 0), the risk value may be calculated by:

${risk} = {{{ris}k_{stanaing}} + {( {1 - {risk_{stanaing}}} )*( {1 - {\cos( \frac{\pi\theta}{180} )}} )}}$

When the user is not reclined or leaning forward (i.e., theta is 0) andis tilted to the left (phi between 0 and −90 degrees), the risk valuemay be calculated by:

${risk} = {{ris}k_{stanaing}*( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*( {{risk}_{standing} + {( {1 - {risk}_{standing}} )*( {1 - {\cos( \frac{\pi\;\theta}{180} )}} )*\sqrt{1 + {\sin( \frac{\pi\;\varphi}{180} )}}}} )}} }$

When the user is leaning forward but not inverted (i.e. 0<theta<90) andphi is 0, the risk value may be calculated by:

${risk} = {( {{risk_{stomach}} - {risk_{standing}}} )*{\cos( \frac{{- \pi}\theta}{180} )}}$

When the user is leaning forward but not inverted (i.e. 0<theta<90) andtilted to the left (phi is between 0 and −90), the risk value may becalculated by:

$ {{risk} = {{{ris}k_{{left}\mspace{11mu}{side}}} + {( {1 - {risk_{{left}\mspace{11mu}{side}}}} )*( {( {{risk_{stomach}} - {risk_{standing}}} )*{\cos\ ( \frac{{- \pi}\theta}{180} )}} )*( {1 + \frac{\pi\varphi}{180}} )}}} )$

When the user is leaning forward by more than 90 degrees (i.e.,180>theta>90) and phi is 0, the risk value may be calculated by:

${risk} = {{{ris}k_{stomach}} + {( {{risk_{headstand}} - {risk_{stomach}}} )*( {{- \cos}\frac{\pi\theta}{180}} )*risk_{headstand}}}$

When the user is flat on their stomach (theta=90) and tilting to theleft (0>phi>−90), the risk value may be calculated by:

${risk} = {{risk}_{{left}\mspace{14mu}{side}} + {( {1 - {risk_{{left}\mspace{11mu}{side}}}} )*risk_{stomach}*( {1 + {\sin( \frac{\pi\varphi}{180} )}} )}}$

When the user is leaning forward by more than 90 degrees (i.e.,180≥theta>90) and tilting to the left (0>phi>−90), the risk value may becalculated by:

${risk} = {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*( {{risk}_{stomach} + {( {{risk}_{headstand} - {risk}_{stomach}} )*( {{- \cos}\;\frac{\pi\theta}{180}} )*{risk}_{headstand}}} )*\sqrt{1 + {\sin( \frac{\pi\varphi}{180} )}}}}$

When the user is tilting to the right side (0>phi>90), the risk valuemay be the risk value at an equivalent position when tilting to the left(risk_(left equivalent)) that factors in the right side risk. Forexample, in the illustrated matrix, when the user is tilting to theright side (0>phi>90), the risk may be calculated by:

${risk} = {{risk}_{{left}\mspace{11mu}{equivalent}} + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}}$

Thus, when the user is reclined backward by between −90 and −180 degrees(−90>theta>−180) and tilted to the right (0>phi>90), the risk value maybe calculated by:

${risk} = {( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*{risk}_{{- 90} < {recline} < {- 180}}*\sqrt{1 + {\sin( \frac{\pi\varphi}{180} )}}}} ) + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}}$

When the user is reclined backward between 0 and −90 degrees or −90degrees (i.e., 0<theta≤−90), and tilted to the right (0>phi>90), therisk value may be calculated by:

${risk} = {( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*( {{risk}_{standing} + {( {1 - {risk}_{standing}} )*( {1 - {\cos( \frac{\pi\theta}{180} )}} )}} )*\sqrt{1 + {\sin( \frac{\pi\varphi}{180} )}}}} ) + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}}$

When the user is not reclined or leaning forward (i.e., theta is 0) andis tilted to the right (0>phi>90), the risk value may be calculated by:

${risk} = ( {{{risk}_{standing}*( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*( {{risk}_{standing} + {( {1 - {risk}_{standing}} )*( {1 - {\cos( \frac{\pi\theta}{180} )}} )*\sqrt{1 + {\sin( \frac{\pi\varphi}{180} )}}}} )}} )} + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}} $

When the user is leaning forward but not inverted (i.e. 0<theta<90) andtilted to the right (0>phi>90), the risk value may be calculated by:

${risk} = {( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*( {( {{risk}_{stomach} - {risk}_{standing}} )*{\cos( \frac{- {\pi\theta}}{180} )}} )*( {1 + {\sin( \frac{\pi\varphi}{180} )}} )}} ) + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}}$

When the user is flat on their stomach (theta=90) and tilting to theright (0>phi>90), the risk value may be calculated by:

${risk} = {( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*{risk}_{stomach}*( {1 + {\sin( \frac{\pi\varphi}{180} )}} )}} ) + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}}$

When the user is leaning forward by more than 90 degrees (i.e.,180≥theta>90) and tilting to the right (0>phi>90), the risk value may becalculated by:

${risk} = {( {{risk}_{{left}\mspace{11mu}{side}} + {( {1 - {risk}_{{left}\mspace{11mu}{side}}} )*( {{risk}_{stomach} + {( {{risk}_{headstand} - {risk}_{stomach}} )*( {{- \cos}\frac{\pi\theta}{180}} )*{risk}_{headstand}}} )*\sqrt{1 + {\sin( \frac{\pi\varphi}{180} )}}}} ) + {{\sin( \frac{\pi\varphi}{180} )}^{2}*{risk}_{{right}\mspace{11mu}{side}}}}$

While these exemplary functions, constants, and constant values areprovided, it should be understood that embodiments of the system are inno way limited to the above functions and the exemplary constants or theexemplary constant values. As stated, the orientation risk values andequations may be customized or refined upon further clinical analysis.Optionally, as discussed above, look up tables may be used to associaterisk values with orientation data. Additionally, it should be understoodthat other risk scales may be used. The exemplary 0-1 scale is providedfor example only and is non-limiting.

Thus, based on the received orientation sensor data, a determination ofan orientation risk value can be made 204. A time series of orientationrisk values 206 may be determined as the sensor data is received. Fromthe time series of orientation risk values, a cumulative orientationrisk value may be calculated 208. In an exemplary embodiment, thecumulative orientation risk value may be the average of the last 300seconds of orientation risk scores. As the device receives the newestorientation risk score, it may discard the oldest, so that the mostrecent 300 seconds worth of orientation risk scores are always averagedto into the cumulative orientation risk value.

The cumulative orientation risk value may then be compared to anorientation risk threshold 210. In some embodiments, the orientationrisk value may be compared to a first threshold and a second threshold.The first threshold may be a medium risk threshold and the secondthreshold may be a high risk threshold. For example, in someembodiments, the medium risk threshold may be between 0.15-0.25,preferably 0.2, and the high risk threshold may be between 0.35-0.45,preferably 0.4.

In further embodiments, a processor could use manual user data inputsand diagnostic test results in conjunction with physiological parametersto assess the risk of disease initiation. Further, the method 200 mayfurther include receiving user data 214 to adjust or calculate one ormore risk thresholds 216. For example, user attributes or pregnancyfactors may increase or decrease orientation risks. In some embodiments,the data from a multitude of users could be used to create a moreaccurate predictive algorithm of risk based on certain physiologicalparameters. For example, the Overall Relative Etiological Risk(ore.risk) may be factored into medium risk and high risk thresholds tochange the threshold at which alarms are triggered. The ore.risk willtypically be a value between ˜0.5 and 10 where 0.5 is a very low riskuser, 1 is an average risk user, and 10 is a very high risk user.

So, the medium risk and high risk thresholds may be modified by theore.risk value in order to create the new threshold alert levels. Forexample and etiological adjust medium risk may be calculated by:eti.adj.med.risk=med.risk/sqrt(ore.risk)

For example, a Low risk user may be not overweight, with low bloodpressure, and on her 2^(nd) pregnancy with an ore.risk score of 0.6.

${{eti}.\mspace{14mu}{adj}.\mspace{14mu}{med}.\mspace{14mu}{risk}} = {{{{med}.\mspace{14mu}{risk}}/{{sqrt}( {{ore}.\mspace{14mu}{risk}} )}} = {\frac{.2}{.78} = {.26}}}$

In a further example, a med (average) risk user (thin, low bloodpressure, 2^(nd) pregnancy) with an ore.risk score of 1.

${{eti}.\mspace{14mu}{adj}.\mspace{14mu}{med}.\mspace{14mu}{risk}} = {{{{med}.\mspace{14mu}{risk}}/{{sqrt}( {{ore}.\mspace{14mu}{risk}} )}} = {\frac{.2}{1} = {.2}}}$

In yet another example, High risk user (thin, low blood pressure, 2^(nd)pregnancy) with an ore.risk score of 9.

${{eti}.\mspace{14mu}{adj}.\mspace{14mu}{med}.\mspace{14mu}{risk}} = {{{{med}.\mspace{14mu}{risk}}/{{sqrt}( {{ore}.\mspace{14mu}{risk}} )}} = {\frac{.2}{3} = {.7}}}$

As can be seen, the medium risk threshold for the alert to be triggeredgets lower as the user becomes increasingly likely to developpreeclampsia. This means they would be more frequently encouraged tolower their activity and remain in lower risk orientations.

The algorithm may take many risk factors into consideration to customizethe alert threshold for each user. The output of this risk etiologicalalgorithm ranges between ˜0.5 and ˜10 where 10 is most likely to developpreeclampsia.

For example, a 2^(nd) time mother who is thin and healthy might have ascore of 0.7 whereas an obese first time mother with chronichypertension might have a score of 5.0. The Overall Relative EtiologicalRisk (ore.risk) may be factored into the various risks calculated by thedevice. The ore.risk may be calculated my multiplying all the relativeexemplary risks together in the exemplary table illustrated in FIG. 12 .The list of factors is exemplary and further the proposed values arenon-limiting. Table 1 illustrates a chart which may be used to informthe user what their risk level is for preeclampsia.

Low Risk 0 to .8 You have a preeclampsia risk less than the averagewoman Medium Risk .81 to 1.5 You have an average risk of preeclampsiaHigh Risk 1.51 to 3 You have an elevated risk of preeclampsia Very High3.1 and higher You have a very high risk of Risk preeclampsiaIn many embodiments a user interface of the device may receive inputfrom the user of these pregnancy factors. Examples of pregnancy factorsinclude, but are not limited to, maternal age, height, weight, bloodpressure, blood oxygen, amount of leg swelling, and due date. A softwareapplication may ask the user to input a given pregnancy factor, couldthen use the factor to customize the processor algorithm.

Accordingly, in certain implementations, a wearable device system foridentifying and/or reducing health risks is provided, the wearabledevice is given having one or more sensors configured for transmissionand receiving of signal data and a signal processor configured toreceive signal data from the sensor and to process the information,wherein the signal processor is programmed to identify sleep disorderedbreathing as a physiological parameter and generate a time-series of1^(st) risk scores based on the characteristics of the sleep disorderedbreathing; generate a time-series of 2^(nd) risk scores which are basedon multiple 1^(st) risk scores over time; and, generate an alert whenthe 2^(nd) risk score crosses a specified threshold value. It isunderstood that the other physiological and psychological parametersdiscussed herein can be similarly assessed for risk scores.

Optionally, the factors may be taken into account by adjusting one ormore of the risk constants in the orientation risk algorithm. Forexample, FIG. 10B illustrates an alternative adjusted or customizedpreeclampsia matrix of orientation risk values for users withincompetent cervix. In the exemplary matrix in FIG. 10B uses the sameunderlying functions of FIG. 10A, but one or more of the six riskconstants may be adjusted. Cervical incompetence is a medical conditionin which a pregnant woman's cervix begins to dilate and efface beforeher pregnancy has reached term. Accordingly, it may be beneficial forpersons diagnosed with incompetent cervix to limit the amount ofstanding. Thus, the standing upright risk constant may be adjustedhigher (e.g., to 0.5) compared to the standing upright risk constant ofFIG. 10A to adjust or customize the matrix of risk values specific tothe needs of the user. FIG. 10C depicts a model graph, showing riskscores in specific positions, where the risk scores are shown on theZ-axis, and the x- and y-axis represent model theta and phi,respectively.

In some embodiments, risk values or risk thresholds could be adjusted toaccount for various other risk factors as well. For example, a positionrisk coefficient for gestational age may be provided. For pregnantusers, different positions may become riskier the later in gestation, sothe following formula may be used for calculating a position riskcoefficient to generate a gestational age modified instant activity riskscore (gam.inst.risk.act) (or a modify an associated threshold), wheregestational age is in weeks.

${{Position}\mspace{14mu}{Risk}\mspace{14mu}{Coefficient}_{{gest}.{age}}} = {\frac{\tanh( \frac{{{gest}.\mspace{14mu}{age}} - 10}{5} )}{2} + 0.5}$

FIG. 16 illustrates a table and plot showing the exemplary relationshipbetween risk coefficients and gestational age. This gestational agemodifier algorithm is exemplary. As this is only applicable to pregnantusers, this modifier may be turned off or on by the user. Other factorsmay be weighed as such as obesity, diabetes, multiple pregnancy, bloodpressure, body mass index, etc. Other factors that may affect thethreshold risk value are discussed further below.

FIG. 11 illustrates an exemplary method 300 for monitoring activityrisks. At step 302 force data is received. The total force experiencedby the user may be determined from the received force data 304. A timeseries of force data may be generated 306. Changes in force may bedetermined using the time series of force data 308. The changes in forcemay be compared to an activity threshold to determine whether the useris engaged in a clinically significant level of activity 309.

To monitor activity risks, an activity risk score may be associated withthe calculated force changes 310. A cumulative activity risk score maybe calculated 312. The cumulative activity risk score may be compared toan activity risk threshold value 314. Based on the comparison, afeedback may be outputted to the user 316. Additionally, a duration ofthe user activity may be recorded for a period of time 318. A cumulativeduration of the user activity over the entire time period may becompared to a preferred amount of user activity 319. Based on thecomparison, a feedback may be outputted to the user 320.

Similar to the orientation monitoring method described above, user datamay be received 322 and the user data may be used to adjust or calculatethe activity risk threshold value 324. Further, the user data may beused to adjust or calculate the preferred amount of user activity 326.The customized activity risk threshold value 324 may be fed into step314 for comparison to the cumulative activity risk score. The customizedpreferred amount of user activity 326 may be fed into step 319 forcomparison to the cumulative user activity over the time period.

In some embodiments, the activity risk may be evaluated based onvigorousness rather than by activity type. Vigorousness may be dependedon the delta in g-force at any point in time. As discussed above, thereceived force data may be F_(x), F_(y), F_(z) force data. In someembodiments, the force data may be received at a frequency between0.5-50 Hz and the received data may be placed into memory for furtheranalysis. Preferably, the data is received at least at a frequency of 10Hz. The total force experienced by the user may be determined from thereceived force data 304 by adding the absolute values of F_(x), F_(y),and F_(z) to generate total g-force.

A time series of force data may be generated 306 and changes in forcemay be determined using the time series of force data 308. For example,a subset of the time series of force data may be defined and the minimumand maximum values may be extracted from the subset to calculate theforce change. For example, a subset may include increments of 15 forcevalues. The changes in force may then be compared to an activitythreshold to determine whether the user is engaged in a clinicallysignificant level of activity 309.

For example, where the force data is received every 0.1 seconds, theforce change may be calculated every 1.5 seconds to determine ifactivity is still occurring and if so, what level of vigorousness. Theaverage g-force when not moving will be ˜1. For the exemplary algorithm,if the total g-force regularly fluctuates from 0.75 to about 1.25 (or adelta of 0.5), over any 1.5 second span, the algorithm may determinethat the user is engaged in clinically significant activity. In someembodiments, a number of thresholds may be used to identify differentlevels of activity. For example, when force changes=0-0.3, the algorithmmay determine that the user is not engaged in activity. When the forcechanges=0.3-0.75, the algorithm may determine that the user is engagedin low activity. When the force changes=0.75-1.25, the algorithm maydetermine that the user is engaged in medium activity. When the forcechanges=1.25-2, the algorithm may determine that the user is engaged inhigh activity. When the force changes>2, the algorithm may determinethat the user is engaged in dangerous activity. These values forvigorousness levels are exemplary.

To monitor activity risks, an activity risk score may be associated withthe calculated force changes 310. An example activity risk algorithm mayprovide a relatively low risk at low force changes (e.g., g-forcechange<0.5) but may then sharply increase and as force changes grows to2 and higher the activity risk score may asymptote to 1. An exemplaryactivity risk algorithm may be:

${{activity}\mspace{14mu}{risk}} = {\frac{- 0.5}{( {{\Delta\;{force}} + 0.1} )^{1.25}} + 1}$

This activity risk algorithm is exemplary and non-limiting.

A cumulative activity risk score may be calculated 312. The cumulativeactivity risk value may be a moving average of the risk scores. Forexample, the cumulative activity risk value may be the average of asubset of the time series of activity risk values (e.g., the last 30seconds—the last 1000 seconds of activity risk values). In oneembodiment, the cumulative activity risk value may be the average of thelast 300 seconds of activity risk scores. As the device receives thenewest activity risk score, it may discard the oldest, so that the mostrecent 300 seconds worth of activity risk scores are always averaged tointo the cumulative activity risk value.

The cumulative activity risk score may be compared to an activity riskthreshold value 314. In some embodiments, the activity risk value may becompared to a first threshold and a second threshold. The firstthreshold may be a medium risk threshold and the second threshold may bea high risk threshold. For example, in some embodiments, the medium riskthreshold may be between 0.15-0.25, preferably 0.2, and the high riskthreshold may be between 0.35-0.45, preferably 0.4.

The duration of the user activity may be recorded for a time period 318.The time period may be a day, two days, a week, two weeks or the like.In some embodiments, it may be preferable to record a cumulativeduration of user activity over the course of a week. The cumulativeduration of the user activity over the entire time period may becompared to a preferred amount of user activity 319. Based on thecomparison, a feedback may be outputted to the user 320.

In some embodiments, user data may be received 322 and the user data maybe used to adjust or calculate the activity risk threshold value 324 orto adjust an instant activity risk score. For example, the activity riskalgorithm may be adjusted to factor in a gestation age. For pregnantusers, exercise may become riskier the later in gestation, so thefollowing formula may be used for calculating an activity riskcoefficient to generate a gestational age modified instant activity riskscore (gam.inst.risk.act), where gestational age is in weeks.

${{gam}.\mspace{14mu}{inst}.\mspace{14mu}{risk}.\mspace{14mu}{act}} = {\frac{\tanh( \frac{{{gest}.\mspace{14mu}{age}} - 20}{5} )}{2} + 0.5}$

This gestational age modifier algorithm is exemplary. As this is onlyapplicable to pregnant users, this modifier may be turned off or on bythe user. Other factors may be weighed as such as obesity, diabetes,multiple pregnancy, blood pressure, body mass index, etc. Other factorsthat may affect the threshold risk value are discussed further below.

Further, the user data may be used to adjust or calculate the preferredamount of user activity 326. For example, in some embodiments where thedevice is used to monitor pregnant user activity, an age of gestationmay be factored in to calculate or determine the preferred amount ofuser activity. For example, early pregnancy (e.g., before 20 weeks ofgestation) may have a different preferred amount of user activitycompared to a preferred amount of user activity during late pregnancy(e.g., after 30 weeks of gestation). In some embodiments, 4 hours orless of intense physical activity per week before 20 weeks may berecommended. Additionally, 2 hours or less of intense physical activityper week may be recommended for pregnant women between 21-30 weeks.After 30 weeks, the device may be configured to discourage any intenseactivity.

In some embodiments, the algorithm may discourage long durations ofmoderate activities and short durations of vigorous activities. In someembodiments, the feedback may be configured to encourage certain lowlevel activities like slow walking and may discourage more vigorous oneslike running. Advantageously, the activity monitor may remind users totake breaks throughout the day based on a schedule and/or based on thelevel of activity experienced to that point that day.

Optionally, the algorithm may have set adjustments to conform to variousdesired physiological parameters. For example, the algorithm may bepreprogramed to be switchable between modified activity monitoring,scheduled rest monitoring, bed rest monitoring, and/or hospital bedrest.

In some embodiments, the time series of orientation risk scores may becombined with the time series of activity risk scores to generate acontinuous time series of risk values 111. For many embodiments, thedevice may generate either an activity risk score or an orientation riskscore throughout the day (e.g., every second or more) to provide acontinuous series of activity and orientation risk scores.

This time series of risk scores may then be used to calculate acumulative daily risk score 112. For example, in some embodiments, thedaily score may start at 100 and drop by an amount equal to 1/(60 s*60min*Hours_(day))*100*“instant risk score” every second of the day. Thecumulative daily risk score equation is exemplary and non-limiting. Theequation may be tuned to revise/update the cumulative daily score morefrequently (e.g., every tenth of a second, every half a second, etc.) orless frequently (e.g., every two seconds, every five seconds, etc.).

The Hours_(day) variable is the time duration over which the cumulativedaily score is calculated. In some embodiments, the Hours_(day) variablemay be between 14-24 hrs, preferably 24 hrs. When the 24 hr duration isused, the device may be programmed to start recording the cumulativedaily risk value at 1:00-5:00 AM in the time zone of the user.

After the cumulative daily health score is calculated, the score may becompared to a daily risk threshold. In some embodiments, a low risk usermay aim to stay above 80, a higher risk user may aim to stay above 90and a user prescribed bed rest (for example) may aim to stay above 95.These thresholds are exemplary and non-limiting. In many embodiments thedaily risk thresholds may be raised or lowered or otherwise customizedfor a user based on user factors.

The continuous time series of risk scores may also be used to calculatea combined moving average risk score 113. Similar to the othercumulative risk scores, the combined moving average risk value may bethe average of a subset of the continuous time series of activity riskvalues and orientation risk values combined (e.g., the last 30seconds—the last 1000 seconds of activity risk values). In an exemplaryembodiment, the combined moving average risk value may be the average ofthe last 300 seconds of activity risk scores and orientation riskscores. As the device receives the newest activity risk score ororientation risk score, it may discard the oldest score, so that themost recent 300 seconds worth of activity risk scores and/or orientationrisk scores are always averaged into the combined moving average riskvalue.

The combined moving average risk score may be compared to a combinedmoving average risk threshold value. In some embodiments, the combinedmoving average risk value may be compared to a first threshold and asecond threshold. The first threshold may be a medium risk threshold andthe second threshold may be a high risk threshold. For example, in someembodiments, the medium risk threshold may be between 0.15-0.25,preferably 0.2, and the high risk threshold may be between 0.35-0.45,preferably 0.4.

When monitoring the orientation, activity, or a combination of the two,the device may utilize multiple cumulative orientation risk values,cumulative activity risk values and/or combined moving average riskvalues. Accordingly, in some embodiments, the device may calculate two,three, four, or more cumulative orientation risk values and mayseparately calculate two, three, four, or more cumulative activity riskvalues. Similarly, two, three, four, or more combined moving averagesmay be calculated. For example, the device may employ cumulative scoresover multiple lengths of time (e.g., last 2 minutes, last 5 minutes,last 15 minutes, etc.). Further each of the cumulative values may beassociated with a different threshold clinical value tolerance before analert is generated.

The sensor data from a plurality of devices may be gathered and thethresholds and algorithms may be further refined. Accordingly, in someembodiments, the system may become more accurate and precise over timeas it collects user data and refines the algorithms and thresholdvalues. In some embodiments, the different positions may be subdividedinto different groups for a regression analysis to compare time spent ineach of those positions to age of gestation at birth.

Various implementations of the system comprise a user feedbackmechanism. FIG. 13 illustrates an exemplary user interface 400 fororientation risk monitoring according to some embodiments. Userinterface 400 may include a 3D CAD image 402. The 3D CAD image mayconstantly rotate to mirror the user's orientation. The user interface400 may further include a real time position risk meter 404. Acumulative orientation risk value or a combined moving average riskmeter 406 may also be displayed. A daily compliance meter 408 may alsobe provided. The daily compliance meter 408 may operate like afuel-gauge—it may start at full and drop throughout the day.

When the device 10 detects activity it may switch from the orientationuser interface 400 to the activity monitoring user interface 500illustrated in FIG. 14 . Similarly, when the device ceases to detectactivity, it may switch from the activity monitoring user interface 500to the orientation monitoring user interface 400. Optionally, bothinterfaces 400, 500 may be displayed to the user with an indication asto which one is active or passive (e.g., highlighted, dimmed, etc.).

User interface 500 may illustrate an activity icon 502. The activityicon 502 may be representative of a running person. As activity scoreincreases, the icon 502 may be displayed as running at a faster speed.The user interface 500 may further include a real time activity riskmeter 504. Similar to user interface 400, user interface 500 may alsoinclude a cumulative activity risk meter or a combined moving averagerisk meter 506. The daily compliance meter 508 may also be provide onthe user interface 500.

In some embodiments, the bottom two meters on user interface 400 anduser interface 500 may be the same. In such configurations, the top halfof the screen may automatically switch between activity 504 and positionrisk 404 meters depending upon whether or not activity is detected.

In one implementation, the device may provide some or all of thefollowing menu hierarchy:

-   -   User Information        -   Email        -   Due date        -   First name        -   Last name        -   Doctor email        -   Maternal birth date        -   Any prior live births? [y/n]        -   Twins or more currently in utero?[y/n]        -   Preexisting hypertension (high blood pressure)?[y/n]        -   Height [feet and inches]        -   Weight [lbs]        -   Diabetes Mellitus?[y/n]        -   Highest Maternal Education Level            -   None            -   Elementary            -   Middle and/or high school            -   College        -   Currently living with baby's father [y/n]        -   Previous Abortion [y/n]        -   Cigarette smoking            -   No            -   1-9 cigarettes per day            -   10 or more cigarettes per day        -   Fetal malformation [y/n]    -   Training Mode    -   Share Data        -   Email recipients:        -   Email user [checkbox](change user email)        -   Email doctor [checkbox](change doctor email)        -   Email other [enter email address]        -   All Data [checkbox] or Date Range [enter 2 dates]    -   Contact Smart Human Dynamics        -   San Francisco, CA based Smart Human Dynamics, Inc can be            reached at [email address]    -   Advanced Options        -   Modify algorithms by gestational age [on/off]        -   Modify algorithms by etiological factors [on/off]        -   Medium level alert [choose value 0 to 1 with 0.2 as default]        -   High level alert [choose value 0 to 1 with 0.4 as default]        -   Calibration sensitivity [choose value 0 to 1 with 0.02 as            default]        -   Temporary Risk Estimator [0 to 1; to 2 decimal places]    -   Calibrate Device

The software may be configured to email the following sets of data onseparate pages of an electronic spreadsheet document (such as, forexample, an Excel® spreadsheet):

-   -   Total time app was used, % of time activity was sensed, and        Daily Cumulative risk score [final score of the day; 1 data        point per day for each of these 3 scores]    -   Daily cumulative risk score on a running basis [numerous points        per day at 10 min intervals; e.g. a total of 60 points if device        was used 10 hrs in one day]    -   Moving average risk score [data points at 2 min intervals; e.g.,        total of 300 data points for one 10 hour day]    -   Instant Position risk score and Instant Activity risk score        [essentially, all the raw data at 1 sec intervals]

All days of data may be combined onto one page (workbook sheet) for eachof the 4 data types for a total of 4 pages of data.

FIG. 15 illustrates another exemplary method and system 600 according tosome embodiments. In the illustrated system 600, a software application602 may be loaded onto the user's personal mobile device. The user'smobile device may be a portable electronics device such as, asmartphone, table computer, PDA, smartwatch, or related device. Thesoftware application 602 may receive input on user risk factors 604. Thesoftware application 602 may calculate overall relative etiological risk(ore.risk) 606 using the user inputted user factors 604. The relativeetiological risk (e.g., a value ranging from 0.5 to 10, for example) maybe calculated from a formula in the software 602 that is based onregression analysis of known risk factors. The software application 602may then output the user's relative risk of preeclampsia 608 (e.g.,output to a display on the mobile device). The relative risk may beidentified using the thresholds and categories in Table 1, for example.

Thereafter the software 602 may send the ore/risk to the monitoringdevice firmware 650. The device firmware 650 may revise defaultthreshold values to factor in ore.risk 652 to provide the etiologicaladjusted medium risk and etiological adjusted high risk. For example, ifmed.risk=0.2; high.risk=0.4; and ore.risk=2.1 then:

-   -   eti.adj.med.risk=med.risk/sqrt(ore.risk)=0.14    -   eti.adj.high.risk=high.risk/sqrt(ore.risk)=0.28

Thereafter, a user's gestation age (gest.age) may be factored into therisk thresholds 654. Continuing with the above example, if gest.age=27(weeks), then:

${{gest}.\mspace{14mu}{adj}.\mspace{14mu}{med}.\mspace{14mu}{risk}} = {{{{eti}.\mspace{14mu}{adj}.\mspace{14mu}{med}.\mspace{14mu}{risk}}*( {\frac{{TANH}( \frac{{{gest}.\mspace{14mu}{age}} - 20}{5} )}{2} + 0.5} )} = 0.13}$${{gest}.\mspace{14mu}{adj}.\mspace{14mu}{high}.\mspace{14mu}{risk}} = {{{{eti}.\mspace{14mu}{adj}.\mspace{14mu}{high}.\mspace{14mu}{risk}}*( {\frac{{TANH}( \frac{{{gest}.\mspace{14mu}{age}} - 20}{5} )}{2} + 0.5} )} = 0.26}$

The device firmware 650 may then monitor activity, orientation, or otherphysiological parameters 656 using the customized thresholds. Instantactivity risk scores may be produced 658. The instant activity riskscores may be associated with a vigorousness level of activity. Instantorientation risk scores may be produced 660. Often, the risk scores areproduced to generate a time series of risk scores.

These risk scores may be combined and fed into the moving average riskscore 662 where the algorithm calculates whether risk score is above thegest.adj.med.risk or gest.adj.high.risk thresholds and alerts the useras appropriate. Further a Daily Cumulative Risk may be calculated usingthe combined time series of risk scores 664.

Optionally, the instant activity and instant orientation risk scores maybe transmitted back to the user's mobile device for storage and/oranalysis 610. For example, the software application 602 may display ameter bar graph corresponding to the instant risk score. The meter bargraph may, for example, display the instant risk scores for the entireday averaged at one minute intervals. The software application 602 mayalso allow user may access this information to provide the user moredetail about the instant risk score.

The moving average risk score may also be transmitted back to the user'smobile device for storage and/or analysis 612. The software 602 mayallow the user to access this information to provide the user moredetail about the moving average risk score. The software 620 may also beconfigured to display a bar graph corresponding to the moving averagerisk scores. For example, the bar graph may display all the movingaverage risk scores for the entire day averaged at one minute intervals.

As shown in FIG. 15 , the daily cumulative risk may also be transmittedback to the user's mobile device for storage and/or analysis 614. Thesoftware 602 may maintain a history of daily cumulative risk for alldays. The software 602 may also be configured to display a bar graphwith all the daily cumulative risk bars for all days of the pregnancy. Acolor of each bar may correspond to the level or risk.

Further, in some embodiments, the instant activity risk score may betransmitted to the user's mobile device for further analysis 616. Forexample, an early pregnancy exercise encouragement algorithm may beprovided 616. For users in their early stages of pregnancy (e.g., 15weeks) a certain amount of vigorous activity may be beneficial andencouraged by the software 602. The preferred level of vigorous activitymay trail off at 15 weeks and may get progressively more restrictivebeyond 15 weeks. A meter may display the cumulative number of minutes ofvigorous exercise each day with the target minutes listed as well.

As shown in FIGS. 17-19 , in certain embodiments, the sensor data isanalyzed over days, weeks or months to capture the dynamic changes insleep disordered breathing rather than the absolute values. In oneembodiment, risk scores or alerts are generated when either the changesbecome significant, the absolute values become significant, or acombination of both. In one example, the absolute snoring values may notreach a critical threshold level; however, since they have increaseddramatically from the original values, a warning may be generated. Insome conditions, especially pregnancy, sleep disordered breathingprevalence increases dramatically compared to pre-pregnancy levels. Inone embodiment, the system analyzes the change in sleep disorderedbreathing levels in order to identify patients that are likely to sooncross the critical threshold level that is considered clinicallysignificant and requires some intervention. In another embodiment, thesystem compares sleep disordered breathing values to a large group ofpregnant women to determine appropriate threshold levels for any givenage of gestation or other patient characteristics.

As shown in FIG. 17 , in certain implementations, the device 10 andalgorithm 700 are configured to receive sensor data 702 an identifysegments of time when the user is engaged in an unhealthy behavior. Inthese embodiments, the algorithm is configured to generate a time-seriesof 1^(st) risk scores based on the sensor data 704, generate atime-series of 2^(nd) risk scores which are based on multiple 1^(st)risk scores over time 706; and, generate an alert when the 2^(nd) riskscore crosses a specified threshold value 708.

Accordingly, the system 1 generates a cumulative nighttime risk scorewherein the score increases only after the user is in a dangerousposition for a certain threshold period of time. In a furtherembodiment, the risk accumulation rate increases the longer the user isin the dangerous position.

As shown in FIG. 18 , in certain implementations, the algorithm 700 canreceive sensor data at a first time (box 710) and second time (box 712),identify breathing pathologies at those times (boxes 714 and 716,respectively) and evaluate each for clinical significance (boxes 718 and720). These significance, or risk scores, can be compared with pastclinical significance (box 724) and a risk assessment can be generated(box 726).

In further implementations of the algorithm 700, and as shown in FIG. 19, sensor data can be received by the processor and analyzed by thealgorithm continuously or periodically (box 730). In either event, thealgorithm can identify breathing pathology (box 732), calculate the rateof change (box 734) and generate a risk score or assessment on the basisof this change in pathology (box 736).

In FIG. 20 , sensor data can be received by the processor and analyzedby the algorithm continuously or periodically (box 740). In eitherevent, the algorithm can identify breathing pathology (box 742),calculate the rate of change (box 744) and simultaneously calculate thesignificance of breathing pathology (box 746) and generate a risk scoreor assessment on the basis of this change in pathology (box 748). Aswould be understood, these various approaches can be applied to any ofthe physiological parameters discussed herein.

In one embodiment, if a user frequently goes into dangerous positionsbut for only short periods of time, the score doesn't accumulate verysignificantly; however, if the user spends the same total amount of timein dangerous positions, but does so in long individual segments, therisk score generated is higher. In one example, over the course of onenight's sleep, a user goes into the supine position 10 different timesfor 5 min each time with 5 min gaps in-between where the user is on herleft side. In this example, a total risk score of 22 out of a max 100 isgenerated. In a second example, a user goes into the supine position 1time for 50 min. In this second example, a total risk score of 71 out ofa max 100 is generated. In these two examples, the user spent the samecumulative amount of time supine during a given night; however the userwho had frequent breaks between the supine position generated a muchlower risk score. One reason why a method of scoring like this would beuseful is in the case of a pregnant women who completely occludes herIVC with her gravid uterus each time she is supine. The restriction inflow to her placenta and fetus could be much more damaging if flow bloodflow was restricted over a long period of time compared to intermittentrestriction.

In certain implementations, the device 10 and algorithm are configuredto identify segments of time when the user is engaged in an unhealthybehavior, apply a formula to this period of time to create a risk value;add all risk values together to generate a second risk value.Accordingly, in certain implementations, a wearable device system foridentifying and/or reducing health risks, the wearable device is givenhaving one or more sensors configured for transmission and receiving ofsignal data; and a signal processor configured to receive signal datafrom the sensor and to process the information, wherein the signalprocessor is programmed to identify sleep disordered breathing; generatea time-series of 1^(st) risk scores based on the characteristics of thesleep disordered breathing, generate a time-series of 2^(nd) risk scoreswhich are based on multiple 1^(st) risk scores over time; and, generatean alert when the 2^(nd) risk score crosses a specified threshold value.

As shown in FIGS. 17-19 , in certain embodiments, the signal data isanalyzed over days, weeks or months to capture the dynamic changes insleep disordered breathing rather than the absolute values. In oneembodiment, risk scores or alerts are generated when either the changesbecome significant, the absolute values become significant, or acombination of both. In one example, the absolute snoring values may notreach a critical threshold level; however, since they have increaseddramatically from the original values, a warning may be generated. Insome conditions, especially pregnancy, sleep disordered breathingprevalence increases dramatically compared to pre-pregnancy levels. Inone embodiment, the system analyzes the change in sleep disorderedbreathing levels in order to identify users that are likely to sooncross the critical threshold level that is considered clinicallysignificant and requires some intervention. In another embodiment, thesystem compares sleep disordered breathing values to a large group ofpregnant women to determine appropriate threshold levels for any givenage of gestation or other user characteristics.

In one embodiment, the sensor data is analyzed to determine whether ornot the user should undergo a more extensive sleep study such as an in auser sleep clinic.

In one embodiment an ECG sensor is added to the system to identify fetalactivity. In one embodiment, this additional data stream further helpsto identify clinically significant levels of sleep disordered breathingin pregnant women that are directly impacting fetal wellbeing.

In one embodiment, the snoring detection is not active when theaccelerometer registers significant movement. In one example, thesnoring detection is temporarily paused when the accelerometeridentifies that the user is moving around in their bed which may causerustling sounds that are difficult to differentiate from snoring sounds.

In one embodiment, the device is connected to a mobile phone wirelessly(BLE for example) and it signals the phone to ring in order to alert theuser if sleep position or sleep apnea scores are too far out of range.

In one embodiment, the system prevents episodes of SDB via vibrationalalerts.

In one embodiment, the system issues recommendations to users the nextmorning when they wake up or before they go to sleep.

In one embodiment, the system identifies the likelihood of the userdeveloping sleep disordered brathing based on the position of a givenuser and how that position correlates to sleep disordered breathingbased on past user data.

In one embodiment, the system has an arterial oxygen saturation (“SpO₂”)sensor that can be used when needed. In one embodiment, the systemreminds the user to activate SpO₂ functionality only on certain nightsdepending upon on the accelerometer, microphone, and SpO2 results overthe past days/weeks/months. In one embodiment, a user may have multipleprevious sleep sessions where their SpO₂ levels are very good and theyalso snore very infrequently; therefore the system would not recommendthat they use the SpO₂ attachment on a given night. Conversely, inanother embodiment, if the system sensed low SpO₂ levels and significantsnoring many nights in a row, the system may recommend that the user usethe SpO₂ sensor on a given night.

In some embodiments, the device may include a training system whichteaches the user which orientations or activities are considered risky.For example, in training mode, the device buzzes once when the userenters a position of risk level 0.2 to 0.39, buzzes twice for risk level0.4 to 0.59, buzzes 3 times for risk level 0.6 to 0.79, and buzzescontinuously for risk level 0.8 to 1.

One or more computing devices may be adapted to provide desiredfunctionality by accessing software instructions rendered in acomputer-readable form. When software is used, any suitable programming,scripting, or other type of language or combinations of languages may beused to implement the teachings contained herein. However, software neednot be used exclusively, or at all. For example, some embodiments of themethods and systems set forth herein may also be implemented byhard-wired logic or other circuitry, including but not limited toapplication-specific circuits. Combinations of computer-executedsoftware and hard-wired logic or other circuitry may be suitable aswell.

In various implementations, the fetal activity can include measurementof the fetal heart rate, the movement of the fetus, including kicks,body movements, rotation, and movements relative to maternal structures.In these embodiments, the fetal activity risk score can include anassessment of how fetal movement occurs over a period of time comparedwith a threshold or baseline.

In various implementations, the signal processor does the following:identifies at least two waveform patterns from the reflected signals,wherein a first waveform corresponds to a first maternal anatomicalstructure and a second waveform corresponds to a first fetal anatomicalstructure; identifies the first maternal anatomical structure and thefirst fetal anatomical structure based on pattern recognition of thefirst waveform and the second waveform; and extracts from the reflectedsignals various indicators of fetal heath and maternal health based onthe determination of at least two waveform patterns and theidentification of the maternal anatomical structure and the fetalanatomical structure.

In various implementations, the fetal monitoring system determines theorientation of the pregnant abdomen; each orientation is assigned adifferent risk category; an alert is issued when the risk crosses athreshold value.

In further implementations, the fetal monitoring system comprising aminimum of a sensor and processor; wherein data from the sensor isinterpreted by an algorithm in the processor to generate a risk score;an alert is issued when the risk score crosses a threshold value.

A fetal monitoring system with one or more sensors for generating sensordata, configured to: identify risk values associated with the sensordata to produce a time series of identified risk values; and calculateand update a cumulative risk value by calculating a moving average for asubset of the time series of identified risk values associated with thesensor data, compare the cumulative risk value to a threshold; andoutput a warning when the cumulative risk value crosses the firstthreshold.

In one embodiment, the device that is described by the above claims is asmall wearable device that is placed on the abdominal skin with anadhesive. The device uses a UWB sensor and a processor to continuouslyor periodically sense the frequency and size of fetal movements. Therisk score formula takes into consideration both the frequency andintensity of fetal movements, so if the fetus moves frequently but noneof the movements are significantly large the alert is triggered. Or, ifthe movements are large but they don't happen frequently, the alarm istriggered. If however the movements are significantly large andsignificantly frequent, then the alarm is not triggered.

Certain implementations of the system therefore have a wearable devicesystem for reducing risks associated with pregnancy, the wearable devicecomprising a UWB sensor configured for transmission and receiving ofsignal data, the sensor comprising at least one antenna; and a signalprocessor configured to receive signal data from the sensor and toprocess the information, wherein the signal processor is programmed toidentify fetal activity. In these implementations, the sensor capturesUWB waveforms which are analyzed by an algorithm which comparessuccessive waveforms to each other. If they are very similar, it isassumed the fetus is not moving significantly. The system can generate atime-series of 1^(st) risk scores less fetal movement results in higherrisk scores based on the characteristics of the fetal activity; generatea time-series of 2^(nd) risk scores which are based on multiple 1^(st)risk scores over time; and, generate an alert when the 2^(nd) risk scorecrosses a specified threshold value, where the threshold may bedifferent for different women depending upon their age of gestation andother user characteristics.

In one embodiment, occasional questions pop up in the mobile app or aresent via email, SMS, or other transmission to the user to ask how theyare feeling about their pregnancy or outlook on life in general (egquick response from 1-10). If one very low or many somewhat lowresponses are given the algorithm alerts doctor that counselling ormedication may be necessary.

In one embodiment, the system 1 provides responses to help uplift theuser. Example phrases include “Pregnancy is an overwhelming experiencebut believing that you will have a positive outcome actually increasesthe likelihood of success. Remember this and know that even on thehardest days of pregnancy that millions of other women are sharing thisjourney with you right now. Stay strong and enjoy your pregnancy. Eventhough it is a difficult road at times, it is one that leads toimmeasurable joy.” Other phrases include more clinical and scientificrationale to stay positive and include actionable options. For example,“Staying positive during pregnancy decreases your risk on preterm birthby 40%. Avoiding drugs, alcohol, and smoking is proven to improve the IQof your future child. If you are depressed or are abusing substances andwould like to talk to one of our pregnancy counselors in a judgment-freeenvironment right now, please click here or call xxx-xxxx.”

In one embodiment, the algorithm calculates depression risk in a similarmanner to orientation or activity risk. If the variable called“Emotional Health” dips below a certain health threshold level, thesystem issues alerts to care providers and/or sends feedback to theuser. This feedback may be in the form of positive phrases to encouragethe user or suggestions of things the user can do to improve theirmental state. In one embodiment, these include suggestions to take adeep breath, perform a mental exercise to focus on things they aregrateful for, take a short walk, give a family member a hug, or envisiontheir future healthy birth and child.

In one embodiment, the system 1 periodically issues multiple choicequestions to the user by way of the device 10, such as through theindicators 24, which can include an LCD or other well-known smart devicescreen operationally integrated with the microcontroller 12 and otherelectronic components, as would be understood. They can be sent directlythrough the mobile app or as an email or text message. In oneembodiment, a question asks “How optimistic are you that you will have ahealthy pregnancy and delivery?” The answers have a range of 10 choicesfrom 1 to 10 where 1 is Very Pessimistic and 10 is Very Optimistic. Inone embodiment, if the user responds with a 1, 2, 3, or 4, the algorithmprompts an additional question to gain more insight into the currentmental state of the user.

In some embodiments, the application or software for the device maycomprise a comprehensive diagnostic and therapy system that suggests avariety of tests and treatments for pregnant women. In some embodiments,based on a user's risk profile, the app suggests the user get adiagnostic test (blood protein markers or genetic based) to see if theyare at high risk for any pregnancy diseases. Manual user data inputs,diagnostic test results, as well as position and activity monitor datamay all feed into one comprehensive algorithm that continually assessesa user's estimated risk for disease initiation and progression andoffers feedback to help manage risk.

In some embodiments, the doctor or care provider can communicatedirectly to the user via the device. This may include recommendations tolower activity or change positions based on data the doctor receivesfrom the device, or may include communication unrelated to the datagenerated by the device. In some embodiments, the daily % compliancewith bed rest and/or reduced activity may be sent to the user and/ordoctor.

In some embodiments, a sensing device may measure user position and maysend that data in real time or periodically (e.g., every few hours) tothe user's phone or other Wi-Fi/Bluetooth device.

In further implementations, the system addresses the treatment ofrespiratory diseases.

Various implementations of the device 10 depicted in FIGS. 1 -XXX areconfigured for use in the treatment and detection of respiratorypathologies, such as COVID-19. COVID-19 (“COVID”) has caused a globalpandemic, with respiratory infections and death or other poor outcomesoccurring throughout the world. Some evidence suggests that COVIDpatients benefit from avoiding sleep in the supine position, andavoiding lengthy sleep in a lateral, or side position. In exemplaryimplementations, the disclosed implementations relate to preventing andpredicting negative outcomes and conditions by providing variousdevices, systems and methods directed at collecting information aboutthe sleeping position of the user and issuing alerts when the user issleeping in disfavored positions such as supine and lateral.

In various embodiments, and as shown in the incorporated references, thepresent disclosure relates to a wearable device for use in tracking bodyposition during sleep. While the examples of the device 10 above havegenerally discussed its use in conjunction with the detection ofpregnancy-related conditions, the various device 10 embodimentsdescribed herein can also be directed to avoiding COVID-19 relatedcomplications or death. It should be understood that embodimentsdisclosed herein may be applicable to preventing other conditions aswell. The claimed subject matter may be embodied in other ways, mayinclude different elements or steps, and may be used in conjunction withother existing or future technologies. This description should not beinterpreted as implying any particular order or arrangement among orbetween various steps or elements except when the order of individualsteps or arrangement of elements is explicitly described.

In certain implementations, the device 10 is temporarily affixed to theuser via one or more adhesive, as discussed above. In variousimplementations, a larger adhesive is attached to the chest of thepatient and a second adhesive is on the wearable device. In certainimplementations, the adhesives are Velcro adhesives and the wearabledevice is removable during the day. In certain implementations thedevice is configured to be worn on the chest, while in alternateimplementations the device is configured to be worn on the back. Incertain implementations, the device is substantially triangular so as toproperly orient the internal components and sensors in view of thecurvature of the back or chest of the patient, as would be readilyappreciated.

In certain implementations, the wearable device 10 is used inconjunction with an enterprise system, that is, for example, amonitoring unit that can monitor several wearable devices, such as in aclinical setting. In such implementations, the central processor 2 alsohas access, via the network 8 or some other communication link, toexternal data sources that may be used to keep the information at theenterprise system, such as a nurses station, current.

As one non-limiting example, in certain implementations a plurality ofwearable devices are in wireless electronic communication with a singlemonitoring unit such that a single healthcare provider or severalhealthcare providers can monitor the plurality of units without enteringthe rooms that the patients are resting, such as from a nurse's station.It is appreciated that more remote connections to a monitoring unit arealso possible, such as via LTE, 4G, 5G, Bluetooth and/or WiFitechnologies, as would be readily appreciated by those of skill in theart.

In exemplary implementations, the disclosed devices, systems and methodsrelate to a wearable device 10 for use by respiratory patients such asCOVID-19 patients to monitor sleep position, as described above.

It is understood that in use according to these implementations, thedisclosed device 10 and systems are used to track patient body positionwhen sleeping, and in particular to provide alerts when a patient beginssleeping in a specific position—such as supine—or lingers in a givenposition for an extended period of time. That is, for example, variousimplementations of the disclosed relate to a device 10 and associatedsystem configured to issue an alert when the patient body moves into asupine position for any extended period of time, as would be understood.

Certain implementations of the device 10 comprise an oxygen saturation(SpO₂) sensor 92 configured to capture SpO2 data utilized by theposition algorithm to preferentially encourage certain positions. Forexample, if a user demonstrates higher SpO2 readings in the proneposition compared to the lateral position, the device 10 or accompanyingsystem can be configured to encourage the user to more quickly return tothe prone position each time they go lateral, such as by shortening thelateral threshold time. Conversely, if a patient has been prone for avery long time (for example 5 hours) and their SpO2 is slowly startingfall, the device can alert them to assess the SpO2 in the new position.Many possible implementations are of course contemplated.

An example of this approach in use operates as follows: each bodyorientation has a set amount of time in which the user is allowed to bebefore an alert is issued. In one embodiment, every single uniquecombination of tilt and recline angles has a different total amount oftime associated with it. In one embodiment, a recline angle of −90degrees with 0 degrees side tilt (supine) has the very lowest amount oftime allowed (for example 15 seconds). A combination of recline angle−75 degrees and side tilt 7 degrees has a maximum time of 30 minutes. Acombination of −35 recline and −82 side tilt has no maximum time value(infinity) because it is considered safe. Every time the user movesorientations, the algorithm instantly calculates how much time the userhas left in that position. The remaining time is transferred andprorated to the new position. So, if the user is supine fora transitoryperiod of time (less than 15 seconds) allowed by the algorithm and thenshifts to another orientation, the alarm will not sound.

In certain implementations, the device 10 may include one or moresensors 14 which determine certain physiological parameters and a thecentral processor 2 that receives and stores orientation data from thesensors 14, 92 and uses an algorithm and/or is configured to estimatethe level of clinical risk over various time scales based on thoseparameters. The wearable device 10 may further include a communicationdevice which conveys periodic updates and alerts to the user on theircurrent risk level, either instantaneously or cumulatively, as describedabove. It is understood that the physiological parameters can includeany of the parameters described elsewhere herein and generally includebody position.

In certain embodiments, the wearable device 10 may be used as apreventative measure in COVID-19 cases to slow the progression ofrespiratory pathology.

In certain implementations, the wearable device 10 is able to collectdata. In further embodiments, the device 10 may use geometricapproximations and/or empirical reference data to determine the force,impulse, or pressure. Further implementations record SpO2 via a sensor92.

Various implementations comprise a variety of different sensors 14 fordetecting physiological parameters, including those relating toorientation and sleep patterns, and psychological condition. The devicemay include one or more sensors which determine the spatial orientationof the user's body relative to the direction of Earth's gravity and amicrocontroller that receives and stores orientation data from thesensors and uses the data to estimate the level of clinical risk overvarious time scales.

Optionally, the sensor data comprises a recline angle θ and a side tiltangle φ. The orientation risk values may be a function of the reclineangle and the sideways tilt angle. In further embodiments, the devicemay include a variety of sensors and accelerometers, which may becapable of monitoring the orientation of the body/various areas of thetorso. In various implementations, the physiological parameters recordedby the sensors can include body orientation. In various implementations,the system is configured to establish various risk thresholds, which canbe based on one or more of these physiological parameters.

In various implementations, the device and system provides feedback tothe user. In some embodiments, the device may provide vibrational,visual, or audio feedback to the user based on past or currentorientation of their body during sleep. Further implementations providefeedback at an enterprise or clinical level, such as to alert healthcareproviders as to the sleeping position of the wearer.

In certain implementations, the device and system performs variousfunctions by way of a system of electronic computing components. In someembodiments, a processor may identify orientation risk values associatedwith the estimated orientations of the body to produce a time series ofidentified orientation risk values. A first cumulative risk value may becalculated and updated by calculating a first moving average for asubset of the time series of identified orientation risk valuesassociated with the estimated orientations of the body. The subset forthe first moving average may have a first size. The first size may be atleast the last 30 seconds of sensor data. In some embodiments, it may bethe last two minutes of sensor data. Thereafter the processor maycompare the first cumulative risk value to a first threshold and outputa warning when the first cumulative risk value crosses the firstthreshold. Optionally, an input may be coupled with the processor 2.

The processor 2 may be further configured to calculate and update asecond cumulative risk value by calculating a second moving average fora subset of the time series of identified orientation risk values. Thesubset for the second moving average may include at least the last 5seconds of sensor data. The processor may compare the second cumulativerisk value to a second threshold and output a warning when the secondcumulative risk value crosses the second threshold.

In some embodiments, the processor 2 may be further configured toprocess the sensor data for the applications listed above by calculatingand updating a moving average for a subset of the time series associatedwith that sensor. The processor may be further configured to compare thedata collected from the various sensors to a data set communicating thesensor outputs for the ideal person of similar age as the user. Forexample, the processor could receive force measurements from a sensor,calculate force changes over time using the received force measurements,and determining whether the user is engaged in clinically significantactivity based on the calculated force changes and an activitythreshold. The processor could then compare the clinically significantactivity to the recommended activity level or threshold for a person ofthe same age.

In some embodiments the device 10 may include an infrared sensor 14coupled with the processor 2. The processor 2 may determine device usein response to infrared sensor data.

The processor may be further configured to combine the recorded timeseries of orientation risk values with the recorded time series ofactivity risk values to generate a continuous time series of riskvalues. A cumulative risk may be calculated on a subset of thecontinuous time series of risk values by calculating a moving averagefor a subset of the continuous time series of risk values. Thereafter,the processor may be configured to compare the cumulative risk to acumulative risk threshold value and output a warning when the cumulativerisk crosses the cumulative risk threshold value. In additionalembodiments, the risk value may be reported to the user without havingbeen compared to a threshold.

Optionally, the processor 2, by executing the logic or algorithm, may befurther configured to perform additional operations. Examples caninclude: when the user is determined to be engaged in activity,recording a cumulative time duration of the clinically significantactivity by the user over a period of time and comparing the cumulativetime duration of the clinically significant activity engaged by the userover the time period to a preferred cumulative activity threshold.

In certain implementations, a first single sided adhesive sheet isplaced on the torso which has a larger surface area than a second doublesided adhesive patch that connects the device to the first sheet. Thefirst sheet can be a piece of 2″×2″ tegaderm (hydrocolloid) or siliconethat is safe for the skin. The second doublesided patch (acrylic orsilicone adhesive on either or both sides) would be the same area as theback of the sensor device (˜1″×1.25″). This arrangement allows thedevice to be removed and placed back on the person without removingadhesive directly on the skin while still maintaining a strong adhesivebond to keep the device securely on the body.

Certain implementations of the device 10 and system can be used to alertparalyzed individuals that they need to change positions in order toprevent pressure sores, as would be readily appreciated. In a furtherexample, in a clinical setting, the device can be configured to send analert to a nurse notifying him or her that a paralyzed patient needs tobe moved.

In various implementations, the device 10 detects coughing, and analyzesthe coughing to determine if it is indicative of serious respiratorypathology via techniques known in the are, such as those discussed athttps://news.mit.edu/2020/covid-19-cough-cellphone-detection-1029.

In various implementations, the wearable device 10 is configured topromote certain body positions in hospital patients, wherein thewearable device communicates with a central hub which relays patientbody position to a central dashboard and alerts healthcare workers of apatient's positional non-compliance.

Various implementations further feature an enterprise beacon system,where several wearable devices 10 in a hospital ward having a network 8such that individual terminals, such as Linux computers, configured torecord the device position and respiration data in order to monitor aplurality of respiratory patients in real time. It is understood that inthese implementations, if a patient is in the wrong position too long asdetected by the algorithm, an alert is issued at the terminal, such asat a nurse's station that identifies that a patient has been in aposition, such as supine, for a time period that exceeds a definedthreshold. Further implementations are of course possible.

Although the disclosure has been described with reference to certainembodiments, persons skilled in the art will recognize that changes maybe made in form and detail without departing from the spirit and scopeof the disclosed apparatus, systems and methods.

The invention claimed is:
 1. A method of preventing supine sleep inCOVID patients in need thereof, comprising: providing a device thatmonitors body position; monitoring patient body position over time; andissuing a supine alert when the monitored patient body position issupine for more than about five minutes.
 2. The method of claim 1,further comprising issuing a lateral alert when the monitored bodyposition is lateral for more than about two hours.
 3. The method ofclaim 2, wherein the supine alert and lateral alert are different. 4.The method of claim 1, further comprising providing a monitoringstation.
 5. The method of claim 4, wherein the monitoring stationcomprises an alarm configured to issue a supine alert when the monitoredpatient body position is supine for more than about five minutes.
 6. Thewearable device of claim 1, wherein the device is configured to detectcoughing.
 7. The wearable device of claim 6, wherein the device isfurther configured to analyze detected coughing for respiratorypathology.
 8. The wearable device of claim 1, wherein the device isconfigured to generate alerts to promote body position in respiratorypatients.
 9. A system for monitoring respiratory patients, comprising:a) a device comprising a body orientation sensor configured to record atime series of body position data comprising a supine position and time;and b) an alert system, wherein the alert system is configured to issuea supine alert when the supine position exceeds a defined timethreshold.
 10. The system of claim 9, wherein the time series of bodyposition data comprises a lateral position and time, and wherein thealert system is configured to issue lateral alerts.
 11. The system ofclaim 10, wherein the supine alerts and lateral alerts are differingnumbers of vibrational pulses.
 12. The system of claim 10, wherein thesupine alerts are issued after about five minutes and the lateral alertsare issued after about one hour.
 13. A wearable device to promotecertain body positions in a user comprising an alert system, wherein thedevice is configured to issue an alert if the user has not shiftedpositions within a certain threshold time unless the user is in a proneposition.
 14. A wearable device to promote certain body positions inrespiratory patients comprising an alert system configured to issue analert if a recorded body position is supine and a time series of dataexceeds a supine threshold.
 15. The wearable device of claim 14, whereinthe respiratory condition is selected from the group consisting ofCOVID-19, ARDS, asthma and lung cancer.